Patent Publication Number: US-9432199-B2

Title: System access determination based on classification of stimuli

Description:
CROSS REFERENCE TO OTHER APPLICATIONS 
     This application claims priority to U.S. Provisional Patent Application No. 61/355,149 entitled SYSTEM ACCESS DETERMINATION BASED ON CLASSIFICATION OF STIMULI filed Jun. 16, 2010 which is incorporated herein by reference for all purposes. 
    
    
     BACKGROUND OF THE INVENTION 
     Authentication techniques are used to ensure that actions, for example accessing a computer or other resource, are performed only by an authorized human or other user. One way that websites and other electronic services authenticate their users is by requiring those users to supply a username and a valid password before being granted access. Typically the password is selected by the user the first time the user visits the site (e.g., as part of a registration process), and may be changed by the user as desired. Unfortunately, users sometimes forget their passwords—especially if the password is complex or used infrequently. Passwords can also be difficult to type, for example if the user is using a client with limited input capabilities. Passwords are also subject to compromise by nefarious individuals, such as through guessing, insecure storage by the website/service, and attacks against the user, such as through keystroke logging. Therefore, even when a service provider observes a valid password being entered, there is a risk that the password has been stolen and is being entered by an attacker. Further, in some circumstances devices are linked with user accounts, and allow access to the account to anyone with access to the device, increasing the risk of unauthorized use of devices, whether by a person or a virus. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       Various embodiments of the invention are disclosed in the following detailed description and the accompanying drawings. 
         FIG. 1  illustrates an embodiment of an environment in which authentication is provided. 
         FIG. 2  is a flow chart illustrating an embodiment of a portion of an enrollment process. 
         FIG. 3  is a chart depicting a set of probabilities for various categories of stimuli with respect to a profile. 
         FIG. 4A  illustrates a set of questions and answers usable to select a set of stimuli. 
         FIG. 4B  illustrates a set of questions and answers usable to select a set of stimuli. 
         FIG. 5  is a flow chart illustrating an embodiment of a portion of an enrollment process. 
         FIG. 6A  illustrates an embodiment of an interface for capturing classification information. 
         FIG. 6B  illustrates an embodiment of an interface for capturing classification information. 
         FIG. 6C  illustrates an embodiment of an interface for capturing classification information. 
         FIG. 7A  illustrates an embodiment of an interface for capturing classification information. 
         FIG. 7B  illustrates an embodiment of an interface for capturing classification information to authenticate a user. 
         FIG. 8  is a flow chart illustrating an embodiment of an authentication process. 
         FIG. 9  illustrates an embodiment of a shopping cart interface. 
     
    
    
     DETAILED DESCRIPTION 
     The invention can be implemented in numerous ways, including as a process; an apparatus; a system; a composition of matter; a computer program product embodied on a computer readable storage medium; and/or a processor, such as a processor configured to execute instructions stored on and/or provided by a memory coupled to the processor. In this specification, these implementations, or any other form that the invention may take, may be referred to as techniques. In general, the order of the steps of disclosed processes may be altered within the scope of the invention. Unless stated otherwise, a component such as a processor or a memory described as being configured to perform a task may be implemented as a general component that is temporarily configured to perform the task at a given time or a specific component that is manufactured to perform the task. As used herein, the term ‘processor’ refers to one or more devices, circuits, and/or processing cores configured to process data, such as computer program instructions. 
     A detailed description of one or more embodiments of the invention is provided below along with accompanying figures that illustrate the principles of the invention. The invention is described in connection with such embodiments, but the invention is not limited to any embodiment. The scope of the invention is limited only by the claims and the invention encompasses numerous alternatives, modifications and equivalents. Numerous specific details are set forth in the following description in order to provide a thorough understanding of the invention. These details are provided for the purpose of example and the invention may be practiced according to the claims without some or all of these specific details. For the purpose of clarity, technical material that is known in the technical fields related to the invention has not been described in detail so that the invention is not unnecessarily obscured. 
     Those who control access to computer or other resources commonly use automated techniques to verify the identity of a person or other user attempting to take an action with respect to the resource, such as accessing account or other information, completing a purchase, transferring money, or other transaction. If a user is unable to remember his or her password, that user typically can request that the password be reset via an automated process by which the identity of the user is attempted to be verified. Assorted schemes exist that attempt to determine whether a particular reset request has been submitted by a legitimate user that has genuinely forgotten his or her password or by a nefarious individual attempting to gain unauthorized access to an account. Unfortunately, some such schemes can be vulnerable to data-mining (e.g., asking for a mother&#39;s maiden name or county in which the user owns property, both of which can be obtained from public records). Other schemes can be vulnerable to guessing (e.g., requiring the user to supply answers to a series of questions at enrollment time such as “what is the name of your pet?” for which there are common responses). If questions attempting to elicit less common responses are used (e.g., “what is the name of your grade school teacher?”) a risk exists that a legitimate user will, in addition to forgetting the password, also forget the information needed to complete a password reset action. Other schemes, such as requiring that the user call a help desk for assistance can be expensive, and can also be subject to social engineering on the part of an attacker. If the user maintains a personal website or makes use of social networking sites, an attacker potentially has even more information to mine (e.g., for pet names, names of friends, former addresses etc.) and thus may have an even easier time gaining unauthorized access to the legitimate user&#39;s account. 
     Described herein are embodiments of an authentication system that makes use of two phases—a learning phase (e.g., occurring in conjunction with and/or prior to the initial registration by the user with the system) and a proof phase (e.g., occurring in conjunction with an attempt to authenticate the user on a subsequent visit). As will be described in more detail below, during the learning phase, various stimuli likely to be of interest to the user are determined and presented to the user for classification. As one example, a user is asked to classify various activities as being “scary” or not. As another example, the user is asked to designate whether the user has visited various locations in his or her lifetime. Typically, most users are neutral about most topics. By using the techniques described herein, users can avoid having to repeatedly mark stimuli as “no opinion.” Sets of stimuli are selected by the system as ones that are collectively unlikely to become classified differently over time by a given person, and in accordance with an associated classification rule. Additionally, the stimuli are selected such that legitimate users will successfully answer authenticating information while at the same time making it unlikely for illegitimate users to correctly do so. 
     The following is a list of example types of “stimuli” for which a user can be prompted to supply a classification. The list is not exhaustive, but provided to illustrate various kinds of information that are representative of the stimuli described herein: 
     (a) images of people, events, locations, food dishes, commercials, full-length movie posters, activities, animals, and other objects; 
     (b) movie clips excerpted from music videos, animations, commercials, full-length movies, activities, and other subjects; 
     (c) sounds corresponding to activities, locations, songs, movies, famous personalities, animals, and other sources of audio; 
     (d) interactive environments, such as games and role playing situations; and 
     (e) other appropriate sensory information, such as information based on vibrations mimicking activities, events. 
     The following is a list of example classification rules a user can be prompted to apply to the stimuli: 
     (a) this is funny/not funny; 
     (b) this is scary/not scary; 
     (c) this is interesting/not interesting; 
     (d) this is good/bad; 
     (e) I do this often/I hardly ever do this; 
     (f) I wish I could do this/I do not have any wish to do this; 
     (g) I have done this/I have not done this; 
     (h) I like this/I do not like this; 
     (i) I know about this/I do not know about this; 
     (j) I have owned this/I have not owned this; 
     (k) I want this/I do not want this; 
     (l) I have seen this image before/I have not seen this image before; 
     (m) I have heard this sound before/I have not heard this sound before; and 
     (n) I agree with this statement/I do not agree with this statement. 
     Classifications of stimuli in accordance with some of the above may be binary (e.g., “I have done this” vs. “I have not done this”). Other classifications may have a wider gradation, such as where a user is asked to what degree they agree with a statement (e.g., “I strongly agree,” “I mildly agree,” “I am neutral,” “a mildly disagree,” and “I strongly disagree”). In some embodiments the user is asked to perform a selection of more and less important events. The following question is an example of that type: “Among the following sports, which one(s) do you prefer watching: auto racing, baseball, basketball, bowling, cricket, football, golf, hockey, soccer, ski jump, figure skating, tennis.” A similar question can be posed to ask which ones the user does not like watching. Yet another example of a technique for collecting user classification information is to provide a question that permits several simultaneous correct entries. For example, “Describe your personality by selecting one or more suitable descriptions: introvert, extrovert, peaceful, worrying, goal-oriented, impulsive, confrontational, shy, passionate.” In this example, some of the terms are in contradiction to each other, such as “introvert” and “extrovert,” while others are roughly synonymous, such as “introvert” and “shy.” 
     In some embodiments the stimuli for which a user&#39;s classification is solicited (especially in the aggregate) have a “high entropy,” meaning that given a random sample of individuals, it would be difficult to guess a particular individual&#39;s response for a particular item, whether statistically, or based on knowledge of basic demographic or other information about an individual that can readily be obtained (e.g., through data-mining of publicly available information). Examples of items with low entropy in their corresponding user classifications include stimulus such as “like vacation,” “dislike pain,” “own a car,” “don&#39;t own a yacht,” “have seen Robin Hood,” and “haven&#39;t played the harp.” Conversely, “like seafood,” “have been to the opera,” and “would like to ride in a hot air balloon” are examples of items with higher entropy. The preceding are examples of stimulus for which a given user&#39;s opinions are unlikely to be guessed by a third party, but are also likely to be remembered by that user. As another example, the user can be shown, during the first phase, one or more unique photographs (e.g. of a strange airplane) and during the second phase presented with additional photos and asked to select which photograph(s) to which the user was previously exposed. 
     If a profile of the user indicates the user watches television, classifications with respect to particular genres of television may be solicited, such as: reality shows, news, sports, sitcoms, dramas, movies, soap operas, game shows, and documentaries. Classifications with respect to specific shows and/or actors can also be solicited. 
     If a collection of users express interest for a specific activity in a statistic sense, then each member of this collection of users can be assumed to have an associated preference. This can be used to select what classifications to elicit. For example, if a collection of users join a baseball discussion forum, then they are all likely to have opinions with respect to baseball, and maybe, by extension, spectator sports in general. It may also be known that such a group of users are commonly equally likely to be interested in skydiving as not being interested in skydiving, whereas a more general group of users may exhibit a much greater lack of interest for skydiving than an interest for the same. Therefore, if a user belongs to the baseball forum, it may be beneficial to solicit categorization information from the user regarding skydiving interests, since this answer is largely unpredictable, and therefore, hard for a knowledgeable attacker to anticipate. At the same time, questions such as whether the user likes baseball, has ever played baseball, or has ever owned a baseball bat can be avoided because their answers are easy for a somewhat knowledgeable attacker to guess. In this example, the decisions of which classifications to solicit are based on the expected behavior of the collection of users to which a given user belongs—the particular interests of the specific user may be quite different from the group as a whole. 
     If a profile of the user indicates the user likes to eat food, classifications with respect to particular cuisines may be solicited, such as: American, Barbecue, Japanese/Sushi, Jewish/Kosher, Vegan, and Vietnamese. If the profile of the user indicates that the user enjoys cooking, various ingredients can be presented instead of/in addition to types of cuisine, such as “How frequently do you cook with curry powder,” or “have you ever cooked a crème brulee.” 
     If a profile of the user indicates the user likes music, the user may be further queried for preferences for or against particular genres of music (e.g., Acoustic, Alternative, Big Band/Swing, Blues, Christian &amp; Gospel, Classic Rock n′ Roll, Classical, Country, Dance/Electronica, Disco, . . . ). Based on the responses, sound clips can be played and the user asked whether or not the user likes the song, recognizes the song, recognizes the performer of the song, knows all the lyrics to the song, or any other applicable classification. As another example, at enrollment time, the user&#39;s preferences for certain genres of music can be collected, and later, during authentication, audio clips representative of those genres can be played back to the user, asking the user to confirm whether or not the user likes songs of the same genre (i.e., not necessarily asking the user whether the user likes a particular song). 
     In some cases, some classifications (e.g., “I like Italian food” and “I don&#39;t like onions”) are observable, such as by family members and coworkers. Some classifications can also leave traces (e.g., a review of a movie or a product, or a discussion on a social networking site about hobbies or upcoming travel plans). However, very few third parties will be able to observe or guess a specific individual&#39;s classification of stimuli across multiple categories. For example, while a co-worker might observe Alice&#39;s preference for Italian food, the co-worker is less likely to know that Alice frequently comedy clubs, has never sung Karaoke, and reads romance novels (or all of that information collectively). 
     In some embodiments, when system  102  learns about a publicly expressed opinion of a user (such as through an online product review) and the system has a configuration of the same user&#39;s classifications relating to or correlated to this expressed opinion, the system can prevent the inclusion of such classifications in an authentication session, or to assign them a lower weight as applicable. The system can also decide to include or not include such classifications based on the perceived threat. For example, if the perceived threat is a piece of malware (which is assumed to be highly automated, but not well aware of the user&#39;s opinions) then the item may not be removed from the classification, and its weight may not be lowered. However, if the perceived threat is a family member attempting to access the account, an assumption can be made that personally expressed preferences (e.g., in the form of product reviews) may be fairly well known by the expected attacker, and therefore the item should either be removed or accorded a lower associated weight. Example ways of making a determination of what type of threat is most likely include reviewing the IP address of access; the type of transaction requested; and recent fraudulent and legitimate activity on the system. 
     The techniques described herein can be used as a secondary form of authentication (e.g., for when the user has forgotten or is otherwise unable to supply an existing password) and can also be used as a primary form of authentication (e.g., instead of a traditional password, PIN, or other credential), as applicable. The techniques described herein can also be used for other purposes, such as risk mitigation (e.g., as an additional check prior to allowing a high value or otherwise suspicious transaction to be completed), the detection of malware, and other appropriate applications. Examples of such other appropriate applications are provided below. 
     Example Environment 
       FIG. 1  illustrates an embodiment of an environment in which authentication is provided. In the example shown, a user (hereinafter referred to as “Alice”) purchases products from online retailer  122  and participates on social networking site  126 . Alice&#39;s son, Bob, has an account with gaming network  128  and has permission to use Alice&#39;s credit card with respect to that site. 
     Alice also banks with Acme Bank, both at typical branch offices, and also online, by visiting Acme Bank&#39;s website  112 . Alice connects to website  112  (and services  122  and  126 ) using her laptop  114  and on occasion also uses her mobile phone  116  to perform tasks such as checking her account balances. In various embodiments, other types of clients (not shown) are used to communicate with website  112  (or other services mentioned herein), such as personal digital assistants, set-top boxes, game consoles, etc. In addition to (and/or instead of) websites, such as website  112 , the techniques described herein may also be used in conjunction with other electronic environments, such as those providing authentication to intranet services, network appliances, game servers, etc. 
     The first time Alice decides to avail herself of Acme Bank&#39;s online banking features, she visits website  112  and commences an enrollment process. She is asked to choose a username and supply information such as her full name, phone number, and bank account numbers. She is also asked to select a password. 
     Introduction to Collection of Additional Profile Information 
     In some embodiments Acme Bank collects additional information about Alice in a manner that is transparent to Alice. For example, Acme Bank may capture the identity and version number of the browser with which she accesses site  112 , the operating system and version number of client  114 , and other information associated with client  114 , such as its IP address, cookie information, browser history information (e.g., to determine topics of interest to Alice), and identities/versions of applications installed on client  114  if available. Other examples of information that can be obtained from client  114  about Alice include a list of songs which she has recently listened to on client  114 , a list of videos she has recently watched, and files she has recently downloaded. The captured information can be used to glean both positive (things that the user likes) and negative (things that the user does not like) classification information. Positive preferences can be expressed by acts, whereas negative preferences are expressed by acts of avoidance (skipping to the next song, not clicking a given advertisement or search result, or interrupting a movie early). 
     Acme Bank can also obtain information about Alice from third party sources in a variety of ways. As one example, Acme Bank may periodically scrape publicly accessible information about Alice from site  126  (such as posts that she makes) and site  122  (such as reviews that she writes). Acme Bank may also purchase information about Alice from an aggregator of personal information such as aggregator  124 . As yet another example, Acme Bank may contract with site  122  (or other applicable partner) to obtain non-public information pertaining to Alice, such as her purchase history. In such a scenario, Acme Bank may share back certain information with online retailer  122  that the retailer can use to augment its own authentication system, as applicable. Other example types of information that can be obtained from third parties include movies currently in Alice&#39;s queue with an online movie service (and/or movies she has previously watched), songs Alice has recently listened to (e.g., via a cloud-based music service), and the length of time spent at various websites (e.g., indicating that Alice spends a great deal of time on news-oriented sites and very little time on sports-oriented sites). In some embodiments at least some of the information is collected via an agent installed on client  114  or integrated into a browser plugin. In some embodiments, Acme Bank may not want to know the exact songs Alice has listened to, etc., as this might be construed as a privacy invasion; but may still be interested in acquiring information about genres of music Alice is interested in—or expresses dislike for—but without knowing which one necessarily, and without knowing more detailed information. 
     In some embodiments Alice is asked to supply various “seed” demographic/psychographic information at enrollment time, discussed in more detail below. 
     Introduction to Stimulus Classification 
     As will be described in more detail below, as part of the enrollment process, Alice will be asked to classify various stimuli. For example, Alice may be shown a picture of a particular comedic actor and asked to indicate whether she finds that actor “funny” or “not funny.” Her response is stored and can be used at a later point to assist in the authentication of Alice. 
     While the stimuli shown to Alice during the enrollment can be selected entirely at random (and/or while the exact same set of stimuli can be presented to all enrollees), it is possible that there are many stimuli for which a given user has no opinion or a very weak opinion. Similarly, it is possible that there are many stimuli for which a user&#39;s classification is easily guessable (either because most users share the same thoughts about the stimuli or because the specific user&#39;s reaction is easy to guess based on rudimentary/public knowledge about the user). The additional information collected by Acme Bank is used to determine stimuli (and/or categories of stimuli) that are likely to be relevant to Alice. In some embodiments, the information collected by Acme Bank is also cross matched against publicly available knowledge about Alice to remove stimuli for which Alice&#39;s classifications would be guessable. As one example, if Alice has a penchant for knitting (e.g., as determined from shopping history obtained from online retailer  122 ), other types of crafting will likely be relevant to Alice as well. If Alice has posted public reviews of candle making equipment, however, stimuli pertaining to candle making may be omitted. As another example, if Alice lives on the West Coast, she is more likely to have eaten sushi (and formed an opinion about particular pieces of sushi) than if she lives in the middle of the United States (or in a country where sushi is not typically eaten). If Alice has posted public reviews of sushi restaurants (e.g., on a restaurant review site), stimuli pertaining to sushi may be omitted. As yet another example, if Alice&#39;s profile with social networking site  126  indicates that she likes romantic comedy movies, Alice is unlikely to have seen very many horror movies. Accordingly, questions such as “have you seen Horror Movie X” are likely to all be answered as “no” by Alice and will not be presented as stimuli during the enrollment process. As yet another example, if Alice&#39;s IP address indicates that she is at a university, questions about classic literature and preferences for or against subjects such as chemistry and foreign languages are more likely to be of interest to Alice. As another example, since Alice does not live in Asia, system  102  will not probe her for her opinion of jackfruit, because she is unlikely to have an opinion on it. Conversely, if Alice did live in Asia, the question would likely be a good one, as the bulk of individuals there would have an opinion and whether or not they like jackfruit would be an approximately 50/50 split. 
     In the example shown in  FIG. 1 , authentication module  104  is configured to capture information at the time of Alice&#39;s enrollment and store that information in database  106 , for example, as a set  108  of classifications of stimuli associated with Alice. Database  106  also stores other information, such as policies  110  to be used by authentication module  104  when determining whether to grant Alice access to banking resources on subsequent visits and/or when determining whether Alice should be permitted to reset or recover her password in the event that she later forgets it. Other data structures may also be used to store classifications  108  and/or policies  110  as applicable. 
     In the example shown in  FIG. 1 , system  102  comprises standard commercially available server hardware (e.g., having a multi-core processor, 4G+ of RAM, and Gigabit network interface adaptors) running a typical server-class operating system (e.g., Linux). In various embodiments, system  102  is implemented across a scalable infrastructure comprising multiple such servers, solid state drives, and other applicable high-performance hardware. 
     System  102  provides authentication services for website  112 . System  102  also provides authentication services for Acme&#39;s bank by phone tool  120 , which allows customers to check their account balances and perform other actions via conventional telephone  118 . In various embodiments, system  102  and website  112  are maintained by two different entities. For example, a website hosting company can maintain website  112  under a contract with Acme Bank, while another entity maintains authentication system  102 . Similarly, when Alice visits website  112 , a portion of the content rendered in her browser may be served by the web hosting company, while another portion of the content ( 120 ) can be served by system  102 . 
     In various embodiments, the infrastructure provided by portions of authentication system  102  is located on and/or replicated across a plurality of servers rather than the entirety of authentication system  102  being collocated on a single platform. Such may be the case, for example, if the contents of database  106  are vast and/or if system  102  is used to provide authentication services to multiple websites/services (e.g.,  112 ,  122 ,  124 ,  126 , and  128 ) and/or other electronic resources not shown. Similarly, in some embodiments the serving of content such as website  112  is provided by system  102 , rather than being provided by a separate system (e.g., provided by a web hosting company). Whenever authentication system  102  performs a task (such as receiving information from a user, authenticating the user, etc.), either a single component or a subset of components or all components of authentication system  102  may cooperate to perform the task. In various embodiments, at least some of sites/services  122 ,  124 ,  126 , and  128  each have associated with them (and/or incorporated into them) their own respective authentication system, instead of contracting with the operator of authentication system  102  to provide authentication services to the respective sites. 
     Phase  1 : Enrollment/Learning 
       FIG. 2  is a flow chart illustrating an embodiment of a portion of an enrollment process. In various embodiments, the process shown in  FIG. 2  is performed by system  102 . The process shown in  FIG. 2  can be performed at an arbitrary time prior to the enrollment of a given user, such as Alice, and can also be performed in conjunction with the enrollment process, such as after the user has supplied a selected username and password but before the enrollment process is complete. The process begins at  202  when data associated with a user is collected. As previously explained, a variety of information can be collected with respect to a given user, and the information can be collected from a variety of sources. As one example, if the operator of system  102  has an agreement with online retailer  122 , Alice&#39;s shopping history with respect to online retailer  122  may be collected at  202 . As another example, suppose Alice has an existing account with online retailer  122  and has an existing purchase history with the retailer. Suppose online retailer  122  subsequently chooses to implement the techniques described herein to offer additional security to its users when they engage in large or otherwise suspicious purchases. At  202 , online retailer  122  would evaluate Alice&#39;s stored shopping history. As yet another example, at  202 , system  102  can receive information from aggregator  124 , can scrape social networking site  126  for publicly available information (and/or obtain private information via an API), and can also pose questions to Alice, examples of which are provided below. 
     At  204 , the data received at  202  is processed to determine a set of stimuli that are likely to be relevant to a given individual. Suppose that the process shown in  FIG. 2  is being used in conjunction with a user of online retailer  122 , Charlie. Charlie has purchased several books on barbequing, has reviewed a motorcycle helmet, and has watched several action movies through retailer  122 &#39;s online video streaming service. This information about Charlie is received at  202 . At  204 , the received information is processed to determine a set of stimuli that are likely to be of interest to Charlie. 
     Illustrated in  FIG. 3  is a chart depicting on one axis various categories of stimuli  302 , and on the other axis, a set of classifications for those stimuli  304  (in this case, “like” vs. “dislike”). Based on the information received at  202 , a determination is made that Charlie fits a profile of a “macho” man between the ages of 20 and 50. Statistically, such people are likely to have no opinion about horses (70%), are extremely likely to dislike bingo (90%), and have a roughly even chance (50/50 and 40/50) of liking skydiving and bookstores, respectively. 
     Based on the information shown in  FIG. 3 , a determination is made that Charlie should be asked to classify stimuli pertaining to skydiving and bookstores, because he is likely to have an opinion about those topics, and what his opinions are are unlikely to be guessable by others. Charlie can also be asked to classify stimuli pertaining to bingo, however, an adversary may have an easy time guessing Charlie&#39;s reaction based on rudimentary knowledge about Charlie. Since 70% of people demographically similar to Charlie have no opinion on horses, a decision is made not to include horse-related stimuli in the set presented to Charlie. 
     Classification by the User 
       FIGS. 4A and 4B  illustrate a set of questions and answers usable to select a set of stimuli. In some embodiments the questions and answers illustrated in  FIGS. 4A and 4B  are used at  202  and  204  in the process shown in  FIG. 2 . Suppose Alice&#39;s son, Bob, has decided to register for an account with social networking site  126 . As part of the enrollment he is asked to choose a username, a password, and to specify his age through the use of a range of ages (e.g., under 13, 14-19, 20-25, etc.). Site  126  has an agreement with system  102  to provide enhanced authentication services on behalf of site  126 . In some embodiments site  126  provides to system  102  Bob&#39;s age range which is used as the sole criteria for selecting a set of stimuli that are likely to be of interest to him. Bob may also be asked as part of enrollment to specify his gender, and/or his gender may be implied based on a comparison of his first name to a dictionary of names, either by site  126  or by system  102 . Suppose a combination of both Bob&#39;s specified age, and an assumption made based on his specified first name, are used to determine which stimuli are likely to be of interest to Bob. As one example, stimuli pertaining to bingo, shoes, fashion, and soap opera television shows will be excluded. Stimuli pertaining to sports teams, automobiles, beer, and computer games will be included. If Bob was under the age of 13, stimuli pertaining to certain foods that children traditionally dislike such as grapefruit, or items about which they are very unlikely to have a preference (such as brands of cigar) would be omitted. Similarly, if Bob indicated he was in the over 50 age group, questions about favorite vacation spots, cruise lines, and scotch preferences would be included. As an alternate example, information about Bob&#39;s client can also be considered when determining which stimuli to present. As one example, if Bob connects to site  126  using a computer running a Linux-based operating system and using a text-based web browser, an assumption can be made that Bob is a sophisticated computer user. If Bob connects to site  126  using a dialup-based Internet Server Provider and uses a very old version of the Internet Explorer browser application, an assumption can be made that Bob is a naive computer user. In addition to determining which stimuli to present to Bob at enrollment, such information about his client can also be used when determining which rules apply to Bob (e.g., because the client he uses indicates that his account is more or less likely to be compromised than a typical user of site  126 ). 
     As part of the enrollment process, Bob can also be presented with additional questions for the purposes of determining which set of stimuli are likely to be of interest to him. Portions  402  and  404  of  FIGS. 4A and 4B  respectively illustrate initial knowledge about Bob. Site  126  knows that Bob is between the ages of 20 and 25 because he specified that information as part of his enrollment. Site  126  (or system  102 ) also made an assumption that Bob is likely a male. To confirm whether or not Bob is a male, he is asked that question at  406  (and  408 ). Based on his response (“male”), Bob is asked whether or not he played football in high school ( 410 ,  412 ). Suppose Bob answers that he did play football in high school ( 414 ). An assumption can be made that Bob is an active individual and may be interested in thrilling activities such as skydiving, mountain biking, motorcycle riding, etc. ( 416 ). Suppose Bob answers that he did not play football in high school ( 418 ). An assumption can be made that Bob is likely not an active individual, and also likely not interested in thrilling activities. ( 420 ). Additional questions are asked based on the responses provided to the football question. To confirm whether or not Bob might be interested in thrilling sports, at  422  Bob is asked if he would ever skydive. Bob answers “no” ( 424 ) and the set of knowledge about Bob is updated ( 426 ). As a result of the questions and answers shown in  FIG. 4A , a set of stimuli that includes active sports and travel, but that excludes extreme sports, cooking, and bingo, will be selected. As a result of the questions and answers shown in  FIG. 4B , a set of stimuli that includes active sports, travel, and college, but that excludes extreme sports, will be selected. 
       FIG. 5  is a flow chart illustrating a portion of an embodiment of an enrollment process. In various embodiments, the process shown in  FIG. 5  is performed by system  102 . The process begins at  502  when a set of stimuli (e.g., such as food items and styles of music) is provided to a user. In some embodiments the user is first asked to choose which type of classification rules the user would like to apply. For example, the user can be asked to select up to four types of rules from the list provided above (e.g., “funny vs. not,” “scary vs. not,” “I do this often vs. not,” and “I own this vs. not.”). If the user selects only one type of rule, then he or she has to classify topics according to this rule during the setup. In some system configurations, the user is not asked what classification rules to use, but these are selected for the user by the system or an administrator. 
     At  504 , classification information associated with at least some of the stimuli is received from the user. At  506 , classification information associated with the user is stored. In some embodiments portions of the process shown in  FIG. 5  are repeated. For example, sets of stimuli may be provided to a user in batches, and corresponding batches of classification information are received and stored as appropriate. In some embodiments, batches of classification information are received/stored in conjunction with a later authentication attempt, when the user has gained access to his or her account, and/or when the user otherwise chooses to update the stored classification information. 
       FIG. 6A  illustrates an embodiment of an interface for capturing classification information. In some embodiments the interface shown in  FIG. 6A  is used in conjunction with the process shown in  FIG. 5 . Items  602  are presented to a user (e.g., at  202 ), such as Alice, at enrollment time, such as via website  112 . Alice classifies the items by selecting the checkbox that corresponds to applicable answer. In some embodiments a default preference of “not sure” or “no opinion” is pre-checked. While in the example shown in  FIG. 6A  a total of 20 questions are shown as being presented, in various embodiments more, fewer, and/or a variable and/or dynamically and/or randomly determined number of questions are presented at enrollment, depending on such factors as the degree of certainty and/or security required, the nature and/or value of the resource(s) being protected, the quality, reliability, etc. of the particular questions presented, etc. 
     In the example shown in  FIG. 6A , Alice has indicated that she thinks that comedian Joe Smith is funny ( 602 ), that she does not like anchovies ( 604 ), that she is unsure of whether she has ever played golf ( 606 ), and that she has never been to Chicago ( 608 ). After making all of her selections, Alice submits her classifications to system  102  (e.g., at  504 ). System  102  stores Alice&#39;s classifications, e.g., in database  108  for later use. Other kinds of information about the user&#39;s life that are neither readily available through data-mining, nor easily guessable include “do you sleep on the left side of the bed,” “do you snore,” “do you read in bed,” etc. Different forms of questions can also be mixed within an interface. For example, Alice may be asked to rate some items on a scale of 1-5, to select 4 items that are cute from a list, and answer various questions using a set of radio buttons. 
       FIG. 6B  illustrates an embodiment of an interface for capturing classification information. In some embodiments the interface shown in  FIG. 6B  is used in conjunction with the process shown in  FIG. 5 . As previously explained, classifications can be binary (e.g., I have done this/I have not done this). Classification information can also be collected using a Likert scale. For example, in the interface shown in  FIG. 6B , instead of answering yes or no questions, users are requested to indicate whether or not they agree with statements on a five point scale, with 1 indicating a strong disagreement with the statement, 3 indicating a neutral opinion, and 5 indicating a strong agreement with the statement. Other expressions of preferences, such as expressions of habit, can also be input in a similar manner. 
       FIG. 6C  illustrates an embodiment of an interface for capturing classification information. In some embodiments the interface shown in  FIG. 6C  is used in conjunction with a portion of the process shown in  FIG. 5 . In the example shown, users are asked to manually enter (e.g., into a form) their own list of items and corresponding classifications. Portion  502  of the process shown in  FIG. 5  is omitted accordingly. In the example shown, a user has been asked to provide a list of at least 10 items that the user routinely purchases and a list of 10 items that the user does not purchase. 
       FIG. 7A  illustrates an embodiment of an interface for capturing classification information. In some embodiments the interface shown in  FIG. 7A  is used in conjunction with a portion of the process shown in  FIG. 5 . In the example shown, the user has been asked to select at least five words or phrases that describe the user&#39;s childhood. The user has selected “active” and “dogs” so far. If the user determines that no additional words are applicable, the user can click on link  702  to be presented with additional words/phrases. In various embodiments, different types of input devices can be used to indicate applicable words such as word  704 . For example, the user can be asked to type in the words or phrases that he considers to be most appropriate, rather than selecting ones presented to him. As another example, a webcam or other eye tracking technology can be used to detect at which regions of the screen the user focuses on (and/or deliberately blinks twice after viewing). As another example, a gaming console can include a motion capture device through which the user can make selections. Other techniques for capturing (and later testing) classification can also be used. For example, in addition to answering written questions via website  112 , classification information can also be captured acoustically (e.g., over the phone, via a series of voice prompts to which the user responds with a spoken “yes” or “no”). Similarly, the user could be played a set of audio clips and asked whether they like or dislike the sample. Other non-text entry methods can also be used, as applicable, such as sliders, rotating wheels, and devices used by the physically disabled. 
     Phase  2 : Authentication (and Malware Detection) 
       FIG. 7B  illustrates an embodiment of an interface for capturing classification information to authenticate a user. In some embodiments, the interface shown in  FIG. 7B  is used in conjunction with the process shown in  FIG. 8 . Suppose Alice, after registering with website  112  (and providing classification information via the interface shown in  FIG. 7A ) stops using online banking for a period of time. She eventually forgets her password. To resume using the features of website  112 , Alice must reset her password. Alice visits website  112  and selects an “I forgot my password” option from the appropriate location on the site. Alice is then presented with interface  750 . In the example shown, Alice is requested to select the items that best describe her childhood. After selecting the appropriate items, she clicks “done” and the information is submitted to system  102  ( 802 ). The items shown in interface  750  need not exactly correspond to the items Alice previously selected. For example, if Alice used interface  700  to enroll her preferences, only a subset of the items might be shown in interface  750 . What percentage of items to show in interface  750  is customizable, e.g., based on policies specified by an administrator. As another example, in some embodiments synonyms of the previously selected words may be shown, such as by showing “doggie” instead of “dog” or “fishing pole” instead of “fishing.” 
       FIG. 8  is a flow chart illustrating an embodiment of an authentication process. In various embodiments, the process shown in  FIG. 8  is performed by system  102 . The process begins at  802  when classification data is received, such as from a user. The received classification data is received by the user having selected radio buttons, checkboxes, etc.; through use of a sliding scale, a ranking of presented items, etc.; or a combination thereof (collectively referred to herein as a “selection” of classification data). At  804 , the received classification data is compared to stored classification data. At  806  it is determined whether an action should be authorized based at least in part on the comparison. 
     Comparing Received Preferences to Stored Preferences 
     A variety of techniques can be used to store and subsequently compare stored classification information (e.g., received from Alice at enrollment) to subsequently received information (e.g., received through interface  750 ). For example, suppose system  102  makes use of the interface shown in  FIG. 6A . In that scenario, system  102  makes use of a total of 20 different things (also referred to herein as “stimuli”), notated individually as q i , where i is a value from 1 to 20. Each question q i  can be associated with a directed graph g i  with d i  vertices. If there is a directed edge from vertex v ij  to vertex v ik , then an entry w ijk  in an adjacency matrix A associated with the graph is set to a pre-set value y ijk ; if there is no such edge then the associated entry is set at a value T (e.g., negative infinity). 
     During the process shown in  FIG. 5 , the user is requested to answer some number of questions q i , or as many as are applicable. These questions may be selected at random from a larger set of questions, which in turn may be selected from a yet larger set of questions based on known behavior or demographics of the user. An answer corresponds to selection of one or more of the vertices associated with this question. A selected vertex is associated with the value 1, while a vertex that is not selected is associated with the value 0. A variable u ij  is used to record these values—u ij  is set to 1 (resp. 0) if the vertex v ij  is (resp. is not) selected in this process. The value U, which is specific to the given user, consists of the entire list of recorded values u ij  for this user. The value U is stored by system  102 . If the user doesn&#39;t answer certain questions (and/or has “no opinion”), such questions can be thought of as questions for which none of the vertices are selected. 
     In some embodiments, the classifications supplied at  504  in the process shown in  FIG. 5  are evaluated to determine whether the enrollment is successful. For example, if the user answers all questions “yes,” all questions “no,” or leaves all questions as “no opinion,” a problem may be indicated. One way of determining whether an enrollment attempt should be deemed successful is as follows. The sum of all (w ijj *u jj ) is computed, for 0&lt;i,j&lt;n+1, where n is the number of questions (e.g., 20). This sum is denoted S 1 . If S 1  is greater than some pre-set threshold value t 1 , then enrollment is considered successful, otherwise not. If the enrollment is not considered successful, then the value U is erased, and the user has to start over. 
     When an authentication is attempted (e.g., when Alice wishes to reset her password), system  102  retrieves the associated value U. Then, the user is requested to answer questions q i . An answer ( 802 ) corresponds to selection of one or more of the vertices associated with this question. A selected vertex is associated with the value 1, while a vertex that is not selected is associated with the value 0. A variable z ij  is used to record these values; thus, z ij  is set to 1 (resp. 0) if the vertex v ij  is (resp. is not) selected in this process. 
     To determine whether a given authentication attempt should be considered successful, the following is performed at  804 : The sum of all (w ijk *u ij *z ik ) is computed, for 0&lt;i,j,k&lt;n+1. This sum is denoted S 2 . At  806 , it is determined whether S 2  is greater than some pre-set threshold value t 2 . If so, authentication is considered successful, otherwise not. 
     The value y ijk  is the “benefit” of selecting outcome j for question i during enrollment, and then subsequently selecting outcome k for question k during authentication. A low value, such as the value T, can be used as a “punishment” for answering a question incorrectly, whereas a higher value is to be interpreted as a reward for answering the question correctly. 
     Numerical Example 
     Suppose q 1 =“Do you think Joe Smith is funny?” with three possible answers “yes”, “neutral,” and “no.” These possible answers correspond to three nodes v 11 , v 12 , and v 13 . All values u 11 , u 12 , and u 13  are set to 0. If the user selects that she thinks Joe is funny, then the value u 11  is set to 1; if she has no strong opinion, then u 12  is set to 1; and if she does not think he is funny, u 13  is set to 1. 
     Additional constraint values are y 111 =3, y 112 =−5, y 113 =T, y 121 =0, y 122 =0, y 123 =0, y 131 −T, y 132 =−6, and y 133 =4. 
     When the user attempts to authenticate, the values z 11 , z 12 , and z 13  are set. The nine combinations of preferences during enrollment vs. authentication are as follows: 
     (YES, YES): S 2 =y 111 =3 
     (YES, NO OPINION): S 2 =y 112 =−5 
     (YES, NO): S 2 =y 113 =T 
     (NO OPINION, YES): S 2 =y 121 =0 
     (NO OPINION, NO OPINION): S 2 =y 122 =0 
     (NO OPINION, NO): S 2 =y 123 =0 
     (NO, YES): S 2 =y 131 =T 
     (NO, NO OPINION): S 2 =y 132 =−6 
     (NO, NO): S 2 =y 133 =4 
     Thus, if the user first says she thinks Joe Smith is funny (during enrollment), and later says she does not during authentication, then the sum S 2  becomes minus infinity. The same thing happens if she says that she does not think Joe is funny during enrollment, and later says she thinks he is. (In various embodiments, the punishment is set to a value of much smaller absolute value. For example, while a correct answer may give 5 points, an incorrect answer may cause the loss of 20 points.) However, if she has no opinion during enrollment, then her answer during authentication always results in the sum S 2 =0. If she has an opinion during enrollment, and no strong opinion during authentication, the sum is set to a small negative value. If the user retains her like or dislike from enrollment to authentication, S 2  is a positive number. 
     The assignment of low absolute weights allows for the later cancellation of incorrect answers to questions that the user has no strong opinion of (e.g., the types of questions where temporal variance is going to be the greatest). The assignment of large negative weights introduce strong negative feedback for questions where users have a strong opinion, but where the answer during authentication is incorrect. The assignment of positive weights allow for the detection of correct answers given during authentication. The assignment of low absolute weights reduces the impact of a small number of incorrect answers during authentication, where the incorrect answers are not contradictory with the users previously stated opinion, but merely not in complete agreement with these. 
     As multiple questions are considered, the sum S 2  corresponds to the cumulative value of all these contributions from the different questions. A sum that is greater than the set threshold t 2  means that the user answered in a similar-enough manner during authentication as she did during enrollment. In some embodiments if the sum is not greater than this threshold, then the user either mistook a YES for a NO (which is unlikely) or vice versa; stated that she had no opinion in the authentication phase for a sufficient number of questions she stated an opinion for in the enrollment phase, or a combination. The threshold t 2  of the authentication phase, and the values y ijk  are set in a manner that balances the risk for false positives with the risk for false negatives, and reflects the degree to which the answers to these questions are estimated to be maintained over time in some embodiments. The threshold t 1  of the enrollment phase is set to guarantee a sufficient number of answers that are not “no strong opinion,” in turn making it impossible to authenticate by answering “no opinion” to all or too many questions. In some embodiments, several values t 2  are used (e.g., one for each type of access right), out of some collection of possible values and types of account access and privileges. The value t 2  can be a function of the value t 1 , and of some minimum value required for access, as well as of other parameters describing the user and his or her risk profile. 
     Questions with more than three possible answers, such as degrees of opinion, and questions that have only two possible answers, and any type of question with multiple answers can be scored by adapting the techniques described herein. 
     In some embodiments instead of assigning the variable y ijk  an arbitrary value describing the associated reward or punishment, a set of values representing y ijk  can be selected and saved. Each such value will be a point in a two-dimensional space, with an x-coordinate and a y-coordinate. For practical purposes, we will assume that all the x-coordinates are distinct, and that all coordinates are represented by integer values in a given range from 0 to p, where p is a system parameter. Associated with each user is a random polynomial f(x) described with random integer parameters in the same range, 0 to p, and where the polynomial is evaluated modulo p. 
     For an instance y ijk  with a large positive value, a large number of points on the curve f(x) are selected and associated with the indices i and k; for a large negative value, a small number of such points are selected; and for a value y ijk  inbetween, an intermediary number of such points are selected. The exact mapping between values of y ijk  and the number of selected points on the curve f(x) is a system parameter that can be customized to minimize false positives and false negatives. The variable R ik  is used to denote the collection of points associated with y ijk , where a large number of points from f(x) is selected if y ijk  is large, and a smaller number of points from f(x) are selected if y ijk  is small. Once a number of points on f(x) has been selected, these are stored in the record R ik , along with random points on a random polynomial f′(x) to fill up those positions that do not contain f(x) values, up to a given maximum number of values that is a system parameter. Here, f′(x) has the same or larger degree than f(x), or corresponds to a random selection of points from the appropriate space. If for each value y ijk  ten points in R ik  are stored, then a high y ijk  (value could be represented by ten points from f(x); a value y ijk  close to zero could be represented by eight values from f(x) and two values from f′(x); and the value T could be represented by ten values from f′(x). The ordering of values from f(x) and f′(x) as they are stored in R ik  would be random or pseudo-random, and not disclosed. 
     Each value y ijk  would be represented in this manner. The matrix of all values R ik  would be saved. This takes the place of the previously defined value U. 
     In the above example, the degree of the polynomial f(x) may be chosen as n*10−1. This means that, for reconstruction of the polynomial f(x) from recorded points, it is necessary to know n*10 points on the curve. The degree of f(x) could, more generally, be chosen as n*L−1−d, where L is the number of points stored per record R ik , and d is an integer value regulating the balance between false positives and false negatives, and corresponds to the total number of values from f′(x) that can be selected as all questions are answered, while still passing the authentication phase. 
     During the authentication phase, the user selects answers. For each such answer, she selects an associated collection of points, in turn associated with the values i (of the question) and k (of the response to the question). During authentication, the machine used by the user does not know what elements are from f(x) and what elements are from f′(x). However, if a selection is made for which R ik  has a large portion of values from f′(x), then it is unlikely that only points from f(x) are going to be selected, and therefore, unlikely that the polynomial f(x) can be reconstructed. If there is a failure, the machine can try another set of points corresponding to the same user selection. A large number of these can be tried. If more than a certain number, say 1000, are tried, then the login script can generate an error to the user and request that the user attempts to authenticate again. An attacker would not have to limit himself to 1000 attempts, but if he has a large number of incorrect selections, he is unlikely to ever be able to reconstruct the polynomial. A machine can determine whether a polynomial is correctly interpolated by trying to compute f(x) on an input value x given by the server. If this is done correctly, then the server will allow the client access, and call the authentication attempt successful. The machines would not have to communicate the values in the clear, but could do this over an encrypted channel, or the client machine may send a one-way function of the result f(x) for the requested value x. Since the server knows the polynomial f(x) as well as x, it can verify whether this is a correct value. It is also possible to use an f-value for a known x-coordinate, such as x=0, as a cryptographic key, provided this point on the curve is never chosen to be part of an entry R ik . Thus, a user that answers a sufficient number of questions correctly would enable his computer to compute f(0) using standard interpolation techniques (and as described above), thereby deriving the key f(0); a computer that fails to compute f(0) would not be able to perform the associated cryptographic actions. Thus, users who fail to authenticate sufficiently well would cause their computer to be unable to perform such actions. 
     In various embodiments other techniques are used to score stored (and subsequently received) preferences. For example, the entropy of a particular question can be used as a weight that is used when computing S 2 . Thus, a question such as “do you sleep on the left side of the bed” may be inherently worth more points (based on its entropy) than a question such as “do you like ice cream.” Special rules can also take into account answers—particularly to life questions—that wrong answer to which may cause an authentication attempt to fail irrespective of the other questions being answered correctly. For example, if Alice indicates that she has been to Chicago at enrollment, and then subsequently denies being there, such an event might indicate that an attacker is trying to impersonate Alice. Conversely, mechanisms can also be used to make sure that for questions the answers to which might evolve over time (e.g., fondness for certain foods considered to appeal only to adults, such as mushrooms and sushi) don&#39;t result in false negatives. 
     Policies 
     A variety of policies can be put in place based on the security and other needs of website  112  (or other appropriate entity such as sites  122 ,  126 , and  128 ). For example, different users may have different personal thresholds for what will constitute a valid authentication and what will not, but certain global minimums can be applied simultaneously. Additionally, different actions can be taken based on factors such as by how much a threshold was exceeded. For example, in a banking context, several thresholds could be used in which if the highest threshold is exceeded, the user is permitted full access to his or her account. If the second highest threshold is exceeded, the user is permitted full access to the account, but a flag is set alerting an administrator to review the user&#39;s account once the user logs off. Other lower thresholds can also be set with their own corresponding set of permitted actions, such as allowing the user read-only access to the account, informing the user that access will only be granted after any additional step is performed (e.g., requiring the user to make or receive a phone call, a respond to a piece of email, send a text message, etc.). 
     Policies can also be used to specify, e.g., how many stimuli a user must classify, and from how many different categories (e.g., music vs. sports vs. food) the stimuli classifications must come. If a large pool of items are available for users to classify, in some embodiments the selection of which items a user is asked to classify is randomized and/or the interface used for authentication is randomized. Policies can also be used to specify whether—if only one or two questions are answered incorrectly during authentication—the stored classification information should be updated to reflect the discrepancy. For example, in some embodiments if a user indicates a liking for a single food that was not previously liked, and does so multiple times, the classification information for that item for that user will be updated automatically to reflect the user&#39;s new classification. 
     In various embodiments users, upon successful authentication, can initiate an “update” process in which they are permitted to repeat the process shown in  FIG. 5  to update classification information. The update process can be triggered by the user (e.g., after coming back from a vacation during which several new things were tried), and can also be triggered by external events, such as attacks on system  102  or any of the websites  122 ,  124 , or  128 , or environmental events, such as a general interest in a particular type of media or information. It is also possible to avoid testing a user (during the proof phase) on aspects that have a temporary bias, such as interest in soccer during the World Cup. 
     System  102  can also draw attention to changed answers (e.g., after the user is successfully authenticated), such as by asking, “Have you changed your mind about mushrooms?” or “Did you recently visit France?” In the case of whether or not the user has seen a particular movie, the system can be configured to be willing to permit or otherwise be more tolerant of “errors” in one particular direction. The tolerance of such errors may relate to the number of classifications of the type that does not correspond to previously recorded information. For example, if the user states that he has seen a large number of the movies for which the system has no information recorded for this user, then it may be that this user has suddenly started to watch a lot of movies, and therefore, the system may be more tolerant to accept recorded non-seen movies as correct selections even if the user says he has seen them during the proof phase. However, other classes are less likely to have this pattern—such as commercials, that have no intrinsic value to the user, and therefore are not sought out; and which are only aired during a limited period of time. 
     Additional information, such as the presence or absence of a cookie, can be use to adjust thresholds (and permitted actions/levels of access) accordingly. As another example, suppose Alice&#39;s phone  116  includes a GPS. In some embodiments a side channel may be used to capture Alice&#39;s location information and to present Alice (e.g. in interface  950 ) with a question that asks if she was in &lt;location&gt; last week. Alice&#39;s GPS coordinates can also be used to determine the selection of the threshold required to pass authentication: a person attempting to authenticate from a location close to Alice&#39;s believed location (e.g., her home address, places she frequents, etc.) may be offered a lower threshold than a person who is distant from all likely GPS positions of Alice. In some embodiments, multiple types of additional information are used/combined. For example, if Alice&#39;s GPS reports that she is currently in California, but her alleged IP address reflects that she is in Romania, the threshold score needed to gain access to her account may be increased, she may be required to supply a username and password, and also provide classification information whereas she might otherwise only be required to supply a correct username/password, etc. 
     In various embodiments, classification information collected by a user is grouped into sets such as “information a family member or close friend might know,” “information potentially subject to shoulder surfing,” “information potentially discoverable by reading third party sites,” etc. Depending on context, during authentication, certain sets of classification information can be excluded from being queried or otherwise discounted. As one example, if Alice has never previously purchased video games on online retailer  122 , but one day attempts to check out a cart with 12 video games, the transaction may be legitimate, but may also indicate that Bob is attempting to buy games without Alice&#39;s permission. In order to verify that Alice is the person attempting the transaction, online retailer  122  (e.g., via system  102 ) may exclude questions answerable by family members (e.g., which side of the bed she sleeps on) and instead ask questions such as the one posed in interface  750 . 
     As another example, suppose gaming network  128  is subject to attacks by nefarious individuals who compromise the usernames and passwords of all users of network  128 . In some embodiments, other sites, such as bank  112  will apply heightened scrutiny to its own users, and in particular, to those (such as Bob) who fit the same demographic of the primary users of site  128 . 
     As yet another example, if an attack is being perpetrated against computers having a particular operating system and/or browser version, visitors to site  122  with client devices that have the properties of those being attacked may be challenged to provide classification information as part of the authentication process, while other visitors whose clients do not have those characteristics will not. 
     As yet another example, if a chance exists that a keystroke logger or screen scraper has been deployed against client  114 , stimuli that has not previously been presented to client  114  (but instead only to client  116 ) may be presented. 
       FIG. 9  illustrates an embodiment of a shopping cart interface. In the example shown, as part of every transaction, or whenever a transaction appears to be suspicious, or whenever otherwise appropriate, instead of selecting a “submit” button to finalize payment, the user is asked to select an image corresponding to something the user likes best. In the example shown in  FIG. 9 , an image of a horse, an image of a book, an image of a knitting needle, and an image of a jet ski are presented. The user may have previously selected one of the stimuli as being preferred (e.g., the user may have indicated a love of horses). Each of the items shown may also be entirely new. For example, if the transaction is not a suspicious one, interface  900  can be used to augment the information stored in database  106  about the user, may be used to test the entropy of questions for future use, and/or may be used for other purposes. By adding new stimuli and by providing only a portion of all known stimuli, an attacker may have a more difficult time guessing which stimuli are associated with Alice, and have a harder time attempting to obtain the correct answers to the questions to be asked in a future authentication session. 
     If the incorrect image is selected, a variety of actions can be taken, including presenting a new set of images (and allowing the transaction to complete if at least ⅔ of questions are answered correctly), an alert can be emailed to the user or sent via SMS, the transaction can be blocked until the user contacts customer service, etc. 
     One use of the interface shown in  FIG. 9  is to reduce “friendly fraud”—that is fraud perpetrated by someone who knows the user. A family member, coworker, or similar individual may have access to the user&#39;s computer but may not know which picture to select, thus offering at least a minimal amount of protection against friendly fraud than an interface which does not include the images. 
     Another use of the interface shown in  FIG. 9  is to probabilistically detect malicious software outbreaks, such as in a geographical region, among members of a demographic or other group, or members of a social network. If detection engine  130  of system  102  notices that, statistically, more failures to pick the correct image are occurring at checkout, a security problem is indicated. 
     Additional Detail 
     Presentation of Stimuli 
     In some embodiments during phase one, a matching collection of concepts are displayed or otherwise made available to the user, along with a description or visualization of the classification rules. For example, if the set is movies seen/not seen, it could be presented as titles, movie poster images, etc. For sounds it could be presented as sounds of nature (water running, wind through trees, thunder); of activities (soccer, ping pong, baseball, a tea ceremony, skiing) or objects (various types of engines, sounds of toys being used) or people and shows (famous actors, jingles), etc. Information may also be collected implicitly, by observing user behavior and actions. This information can later be used to automatically perform classifications to be added to the user&#39;s profile and to identify what information to acquire to obtain better classifications. The information can be acquired from the user or from third parties, or a combination thereof. 
     Disabling Stimuli 
     As explained above, system  102  is configured to attempt to select stimuli which is likely to be relevant to the user (i.e. about which the user is likely to have an opinion of some kind) In some embodiments the user is able to remove certain classes of stimuli. For example, if the user&#39;s browsing history suggests that he likes soccer, then it may be that soccer is preselected as a stimulus, and/or other stimulus related to soccer may be preselected. The selection may be inappropriate because, for example, the user may have temporarily loaned his computer to a friend who is enthusiastic about soccer while the user himself is not. The browsing patterns may be knowable for a variety of reasons: the collection of intelligence can be performed using an application, piece of software or browser plugin on the machine used by a particular person; it can also be performed by a search engine or common portal; by automated querying of the browser history or browser cache of the user&#39;s device, as is possible for some browser types; and any other appropriate technique. As another example, music preferences can be collected from a music playing application; from a website serving music, such as Pandora; from a website that sells music, such as site  122 ; the responses to offers received by email, SMS, or in website advertisements; or by a crawler that identifies preferences expressed on social networking websites, on discussion forums, etc; or by correlation with observed facts in one of these or similar ways. 
     It could also be the case that the user has such strong opinions about soccer that an adversary would easily be able to glean the user&#39;s viewpoints through public channels. In such a scenario, score-related stimulus can be either not preselected, or can be prevented from being selected by the user as a category of stimulus. One way this could occur is as follows. Suppose social networking site  126  has implemented its own version of system  102  to handle password reset requests and to handle login attempts where the IP address of the user is significantly different from previous IP addresses used by that user. Site  126  has access to both public and private postings made by the user and has access to information such as the user&#39;s favorite books, favorite music, and favorite movies. In some embodiments site  126  periodically evaluates each user&#39;s data to glean whether any of the stimuli classified by the user are also present in a meaningful way within that user&#39;s social networking profile (either publicly accessible or privately accessible). If a user&#39;s opinions with respect to such stimuli are available, site  126  can automatically remove them from the list of stimuli provided to the user for secondary authentication purposes. In some embodiments different thresholds are used in determining whether to disable a set of stimuli, such as having a lower threshold to disable information that is posted publicly, while having a higher threshold to disable information that is made available only to friends. In some embodiments, rather than disabling the set of stimuli entirely, the assurance provided by correct answers pertaining to the stimuli are discounted, requiring that more total questions be answered correctly. 
     Thresholds 
     As explained above, as part of the second phase, the user is presented with a subset of the already classified stimuli, and asked to classify them. If a sufficient number of reclassifications are correct, or sufficiently correct in the case where there are degrees of correctness, then the user authentication is accepted as valid. The definition of “sufficient” may depend on the context, i.e., the application or physical environment that the user is requesting access to. It may also depend on past history in terms of authentication attempts and other information about the user and the threat situation. For example, a user who often succeeds at the authentication process very well may be treated differently than one who often succeeds with a marginal pass. The latter is known to make more errors. The threshold may be increased for the former, and there may be a lower tolerance for number of tries. 
     Additional Uses 
     A variety of uses of the technology described herein is presented above. Yet more uses can also be made of the technology. For example, the techniques can be used to unlock or generate a value that is used to unlock a decryption key in a secured processor, where the key is used to decrypt the contents of storage units associated with this processor. The techniques can also be used to cause the unlock of the decryption key. It is possible to use the techniques described herein in conjunction with other transactions, such as decryption of contents, access to resources, or computation of otherwise obfuscated information. One can use the disclosed technology to authenticate to a device that locks parts of a system down to deter theft of data or services. One can also use it to gain access to changing configuration of devices, such as routers. 
     User profiles can be copied from devices already in use to newly deployed devices, thereby limiting the extent to which users have to be engaged in setting up profiles; and limiting the need for observation and learning phases. 
     In addition to the entities illustrated in  FIG. 1 , other users of the technology described herein include health insurance and medical providers; email and online service providers; consumer devices, such as cameras, phones and computers; components of systems, such as processors, co-processors, and routers; medical components such as surgically implanted sensors and medication devices, pace makers, etc; access control devices, such as car locks, ignition keys, house and office locks; and logging services that collect information about accesses, requests and traffic. 
     In one example embodiment, Alice starts using an online service provider. Some time after establishing her account, she is asked to set up a profile that will allow her to reset her password. To do this, she chooses some number of classification rules through an interface provided by the online service provider. In addition to the rules selected by Alice, the online service provider&#39;s system selects additional classification rules for her. For example, Alice chooses “Things that are funny/not funny” and the system chooses on Alice&#39;s behalf the rule “Things I know how to do/do not know how to do.” Next, Alice is shown a collection of images. She can drag each image to a portion of the screen that is labeled “Things I like,” or another portion of the screen labeled “Things I do not like.” In addition, she can pull the same items to a portion labeled “Things I know how to do” or a portion labeled “Things I do not know how to do.” She can pull one item, such as an image of a soccer player kicking a ball, to both “Things I like,” and “Things I do not know how to do,” or some other combination, or to only one category. She can also avoid classifying this image. Alice is asked to classify some number of images in this way; these may be organized in directories and subdirectories. After she has classified a sufficient number of these, her profile is complete. If she wants to, she can classify an insufficient number at one time, and then return to continue classifying more at a later time. It is also possible for the system to observe her behavior or preferences and made preliminary classifications. In some embodiments Alice is asked to confirm these observations. In some embodiments the items that are made available to Alice to classify depends on her observed behavior, or on the observed behavior of other users. The latter may be used to avoid having a too large or too small number of users classify a given item, for example. 
     As another example scenario, suppose Alice has bought a new phone, and made some number of phone calls. Snippets of conversations from the people she called can be chosen and provided to Alice, after which she can classify the voices according to a rule, such as “I know this person well/I do not know this person well.” The system can also select voice snippets from persons Alice calls numerous times and determine that Alice must know these people well, since she speaks to them so often. Similarly, the system can conclude that some other voices, for people Alice does not speak to often, correspond to people Alice does not know well. Some number of these inferences may be incorrect, but a large portion are likely to be correct. System  102  or another appropriate device stores the voice snippets and the corresponding classifications, along with information such as whether it was Alice who made the classification, or the system. This will be stored in Alice&#39;s profile, in which some identifier of hers, such as her phone number, may also be stored. 
     Assume now that Alice has forgotten her password to access some resource, such as a resource on the phone. This resource may be the address book, or the right to read emails sent to Alice. Alice then indicates this fact, and is provided with a list of icons that she can click, which plays the corresponding voice snippets. She is asked to select the icons that correspond to people she knows well. Alternatively, she may be asked to select the five for which she knows the corresponding people best. She makes this selection, and it is compared to the selection in her profile. If there is a sufficiently large agreement between these, then she is admitted to the resource. Note that it is not necessary to display icons. In an alternative embodiment of the authentication phase, Alice may simply be played some collection of voice snippets and be asked to say yes or press 1 if she knows the person reasonably well, or say no or press 0 if she does not. Here, the verifier may be a piece of software or hardware on Alice&#39;s phone. In various embodiments, enrollment of the snippets includes associating a name with the snippet and asking Alice to recall the person&#39;s name or other categorical information, such as “This is my barber” or “This is Bill” or “This is a relative” or “This is my father.” 
     Although the foregoing embodiments have been described in some detail for purposes of clarity of understanding, the invention is not limited to the details provided. There are many alternative ways of implementing the invention. The disclosed embodiments are illustrative and not restrictive.