Patent Publication Number: US-10325114-B2

Title: Computing system with information privacy mechanism and method of operation thereof

Description:
CROSS-REFERENCE TO RELATED APPLICATION(S) 
     This application claims the benefit of U.S. Provisional Patent Application Ser. No. 62/067,928 filed Oct. 23, 2014, and the subject matter thereof is incorporated herein by reference thereto. 
    
    
     TECHNICAL FIELD 
     An embodiment of the present invention relates generally to a computing system, and more particularly to a system for information privacy. 
     BACKGROUND 
     Modern consumer and industrial electronics, especially devices such as televisions, smart phones, cellular phones, portable digital assistants, tablet computers, laptop computers, and combination devices, are providing increasing levels of functionality to support modern life including the protection of private information for the device users. Research and development in the existing technologies can take a myriad of different directions. 
     As more systems become more reliant on services external to the user&#39;s device for analysis and handling of a user&#39;s information, such as recommendations to a user base on a user&#39;s historical data, the risk of theft or attacks on the user&#39;s information become a greater threat. Furthermore, additional measures are needed to provide security for transmitting a user&#39;s personal, private, or sensitive information to systems, servers, services, or a combination thereof external to the user&#39;s device. 
     Thus, a need still remains for a computing system with an information privacy mechanism to protect a user&#39;s information. In view of the ever-increasing commercial competitive pressures, along with growing consumer expectations and the diminishing opportunities for meaningful product differentiation in the marketplace, it is increasingly critical that answers be found to these problems. Additionally, the need to reduce costs, improve efficiencies and performance, and meet competitive pressures adds an even greater urgency to the critical necessity for finding answers to these problems. 
     Solutions to these problems have been long sought but prior developments have not taught or suggested any solutions and, thus, solutions to these problems have long eluded those skilled in the art. 
     SUMMARY 
     An embodiment of the present invention provides a control unit configured to: obtain an information release setting for a raw user information, the raw user information including an information attribute; determine an information format for the information attribute of the raw user information; determine a privacy notion based on the information release setting; generate perturbed user information from the information attribute based on the privacy notion, wherein the information format for the raw user information is preserved in the perturbed user information; and a communication unit, coupled to the control unit, configured to transmit the perturbed user information. 
     An embodiment of the present invention provides obtaining an information release setting for a raw user information, the raw user information including an information attribute; determining an information format for the information attribute of the raw user information; determining a privacy notion based on the privacy setting; generating perturbed user information from the information attribute based on the privacy notion, wherein the information format for the raw user information is preserved in the perturbed user information. 
     An embodiment of the present invention provides a non-transitory computer readable medium including instructions executable by a control circuit for a computing system, the instructions comprising: obtaining an information release setting for a raw user information, the raw user information including an information attribute; determining an information format for the information attribute of the raw user information; determining a privacy notion based on the privacy setting; generating perturbed user information from the information attribute based on the privacy notion, wherein the information format for the raw user information is preserved in the perturbed user information. 
     Certain embodiments of the invention have other steps or elements in addition to or in place of those mentioned above. The steps or elements will become apparent to those skilled in the art from a reading of the following detailed description when taken with reference to the accompanying drawings. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  is a computing system with information privacy mechanism in an embodiment of the present invention. 
         FIG. 2  is an example of the first device in communication with the second device of the computing system. 
         FIG. 3  is an example of a display interface of the first device of  FIG. 1 . 
         FIG. 4  is an exemplary block diagram of the computing system. 
         FIG. 5  is an exemplary architecture diagram for the computing system of  FIG. 1 . 
         FIG. 6  is an exemplary flow chart representing the computing system of  FIG. 1 . 
         FIG. 7  is the information perturbation step of  FIG. 6 . 
         FIG. 8  is a flow chart of a method of operation of a computing system in an embodiment of the present invention. 
     
    
    
     DETAILED DESCRIPTION 
     An embodiment of the present invention provides generating the perturbed user information from the raw user information eliminates the need for encryption or other security measure to protect the raw user information. The perturbed user information has been generated to be maximally different from the raw user information but preserve sufficient correlation to provide accurate recommendations and analysis based on the privacy release policy, which eliminates the need for additional security or protective measures, such as encryption. 
     A further embodiment of the present invention provides that the method of collaborative filtering based on matrix factorization can be implemented in the absence of additional user information based on the public category factor and the user category factor. Since the latent factors for matrix factorization correlate closely with category information, such as the public category factor and the user category factor, the computing system can generate the perturbed user information to resemble public information based on the product of the public category factor and the user category factor. 
     The following embodiments are described in sufficient detail to enable those skilled in the art to make and use the invention. It is to be understood that other embodiments would be evident based on the present disclosure, and that system, process, or mechanical changes may be made without departing from the scope of an embodiment of the present invention. 
     In the following description, numerous specific details are given to provide a thorough understanding of the invention. However, it will be apparent that the invention may be practiced without these specific details. In order to avoid obscuring an embodiment of the present invention, some well-known circuits, system configurations, and process steps are not disclosed in detail. 
     The drawings showing embodiments of the system are semi-diagrammatic, and not to scale and, particularly, some of the dimensions are for the clarity of presentation and are shown exaggerated in the drawing figures. Similarly, although the views in the drawings for ease of description generally show similar orientations, this depiction in the figures is arbitrary for the most part. Generally, the invention can be operated in any orientation. The embodiments have been numbered first embodiment, second embodiment, etc. as a matter of descriptive convenience and are not intended to have any other significance or provide limitations for an embodiment of the present invention. 
     Referring now to  FIG. 1 , therein is shown a computing system  100  with information privacy mechanism in an embodiment of the present invention. The computing system  100  includes a first device  102 , such as a client or a server, connected to a second device  106 , such as a client or server. The first device  102  can communicate with the second device  106  with a communication path  104 , such as a wireless or wired network. 
     For example, the first device  102  can be of any of a variety of devices, such as a cellular phone, a smart phone, a notebook computer, a tablet computer. The first device  102  can couple, either directly or indirectly, to the communication path  104  to communicate with the second device  106  or can be a stand-alone device. 
     The second device  106  can be any of a variety of centralized or decentralized computing devices. For example, the second device  106  can be a laptop computer, a desktop computer, grid-computing resources, a virtualized computer resource, cloud computing resource, routers, switches, peer-to-peer distributed computing devices, or a combination thereof. 
     The second device  106  can be centralized in a single room, distributed across different rooms, distributed across different geographical locations, embedded within a telecommunications network. The second device  106  can couple with the communication path  104  to communicate with the first device  102 . 
     For illustrative purposes, the computing system  100  is described with the second device  106  as a computing device, although it is understood that the second device  106  can be different types of devices. Also for illustrative purposes, the computing system  100  is shown with the second device  106  and the first device  102  as end points of the communication path  104 , although it is understood that the computing system  100  can have a different partition between the first device  102 , the second device  106 , and the communication path  104 . For example, the first device  102 , the second device  106 , or a combination thereof can also function as part of the communication path  104 . 
     The communication path  104  can span and represent a variety of networks and network topologies. For example, the communication path  104  can include wireless communication, wired communication, optical, ultrasonic, or the combination thereof. Satellite communication, cellular communication, Bluetooth, Infrared Data Association standard (IrDA), wireless fidelity (WiFi), and worldwide interoperability for microwave access (WiMAX) are examples of wireless communication that can be included in the communication path  104 . Ethernet, digital subscriber line (DSL), fiber to the home (FTTH), and plain old telephone service (POTS) are examples of wired communication that can be included in the communication path  104 . Further, the communication path  104  can traverse a number of network topologies and distances. For example, the communication path  104  can include direct connection, personal area network (PAN), local area network (LAN), metropolitan area network (MAN), wide area network (WAN), or a combination thereof. 
     Referring now to  FIG. 2 , therein is shown an example of the first device  102  in communication with the second device  106  of the computing system  100 . In this example, the first device  102  is depicted as a mobile device, such as a smart phone, although it is understood that the first device  102  can be other electronic devices, such as a television, desktop computer, laptop computer, or tablet computer. 
     The first device  102  can store raw user information  210  grouped under an information classification  212 . The raw user information  210  is information associated with or about the user or owner of the first device  102 . The raw user information  210  can include personal information about the user. For example, the raw user information  210  can include the name of a movie watched or item purchased by the user; recorded medical or health sensor reading, such as heart rate or blood pressure measurements; or the name or coordinates of a visited location. For brevity, the user hereinafter refers to the user or owner of the first device  102 . 
     The raw user information  210  can be information about the user that has not been altered or changed by the first device  102 , such as information that has not been encrypted or protected with an encryption program. As a specific example, the raw user information  210  can be unmodified information that has been input by the user or gathered by the first device  102 . 
     The information classification  212  defines or identifies the raw user information  210 . For example, the information classification  212  for the raw user information  210  can be movies, items purchased, sensor information, locations visited. 
     For illustrative purposes, this example will depict the information classification  212  as movies viewed by the user and the raw user information  210  as the names of movies. It is understood that the information classification  212  and associated raw user information  210  can be any of those described above. 
     The raw user information  210  can include information attributes  216  of an attribute category  214 . The attribute category  214  is a classification of an attribute or a characteristic. Examples of the attribute category  214  can include the genre or user ratings for media, such as moves, music, or television programs; item type or classification; sensor type, location types. 
     The information attributes  216  are descriptions or characterizations of the raw user information  210  for a given type of the attribute category  214 . For example, the information attributes  216  can be a specific property or characterization of the raw user information  210 . The raw user information  210  can include one or more of the information attributes  216  for a given instance of the attribute category  214 . In a specific example, for the raw user information  210  including “McDonalds,” the attribute category  214  can be “locations” and the information attributes  216  can include “restaurant,” “fast food,” “American food,” “hamburgers,” or other attributes or descriptions of “McDonalds.” 
     Table 1 is an example of the raw user information  210 , the information classification  212 , the information attributes  216 , and the attribute category  214 . In this example, the information classification  212  can be “recently watched movies” and the set of the raw user information  210  can be the list of movies watched by the user, such as “Godzilla,” “The Amazing Spider-Man 2,” “X-men: Days of Future Past,” and “Frozen.” The attribute category  214  is represented as the movie genre and the information attributes  216  are represented as the specific genres types associated with the movie. For instance, the raw user information  210  of “Godzilla” includes the information attributes  216  of “Action,” “Sci-Fi,” and “Thriller.” 
     For illustrative purposes, Table 1 depicts the raw user information  210  having one instance of the attribute category  214 , although it is understood that the raw user information  210  can include more than one of the attribute category  214 . For example of movies, the raw user information  210  can include the attribute category  214  of “user ratings” or “number of times viewed by the user.” 
     The computing system  100  can enable protection of the raw user information  210  by converting the raw user information  210  and associated instances of the information attributes  216  to perturbed user information  218 . The perturbed user information  218  is the raw user information  210  that has been anonymized to resemble the information of a generic user or public user rather than device user. For example, the computing system  100  can generate the perturbed user information  218  by preserving trends in the raw user information  210 , the information attributes  216 , or a combination thereof while modifying, adding, or removing instances of the raw user information  210  and associated instances of the information attributes  216 . As a specific example, random noise can be added to the raw user information  210 , the information attributes  216 , or a combination thereof, which can maintain properties of the raw user information  210  while randomly altering exact content of the raw user information  210 . The functions associated with generating the perturbed user information  218  will be discussed below. 
     The raw user information  210  can be modified in a number of ways to generate the perturbed user information  218 . Table 2 is an example of the perturbed user information  218  generated from the raw user information  210  of Table 1. As illustrated in Table 2, the number of entries in the perturbed user information  218  can be modified from four entries for the raw user information  210  to three entries. Further, the content of the raw user information  210  can be changed, such as changing the name of the movie viewed by the user. Yet further, the information attributes  216  and the number of the information attributes  216  associated with the raw user information  210  can be changed. 
     In this example illustrated in Table 2, the perturbed user information  218  is generated to preserve the trend of movie genres in the raw user information  210  even though the content of the raw user information  210 , which are the names of actual movies watched, and the associated instances of the information attributes  216  have been changed. Furthermore, the perturbed user information  218  can be generated to preserve an information format  220  for the raw user information  210 . 
     The information format  220  is the structure of the raw user information  210  and the perturbed user information  218 . The information format  220  can be based on the information classification  212  and the attribute category  214 . For example, the information format  220  for the raw user information  210  can include the raw user information  210  for a particular type of the information classification  212  that is associated with the information attributes  216  of a particular type of the attribute category  214 . In the example of Table 1, the information format  220  can include the information classification  212  of movies that are associated with the attribute category  214  of movie genres. To continue the example, the perturbed user information  218  of Table 2 includes modifications to the raw user information  210  and the associated instances of the information attributes  216 . However, the information classification  212  of movies and the attribute category  214  of movie genres were preserved in generating the user perturbed user information  218 . 
     The first device  102  can transmit the raw user information  210 , the perturbed user information  218 , or a combination thereof to the second device  106 . For example, the first device  102  can transmit the raw user information  210 , the perturbed user information  218 , or a combination thereof through the communication channel  104 . In this example, the second device  106  is depicted as a recommendation service device. The second device  106  can receive the raw user information  210 , the perturbed user information  218 , or a combination thereof and perform further analysis without further unscrambling or decryption. For example, the second device  106  can provide recommendations for other movies that the user may prefer based on the perturbed user information  218 . 
     The computing system  100  can include the option of an information utility setting  222 , as depicted by the dashed box, for generation and release of the perturbed user information  218 . The information utility setting  222  is a setting that constrains the degree of modification to the raw user information  210  for generation of the perturbed user information  218 . More specifically, the information utility setting  222  can be a constraint based on the quality or degree of accuracy for results generated from analysis or processing of the perturbed user information  218 . Analysis or processing of the perturbed user information  218 , for example, can include the generation of recommendation for other movies based on the perturbed user information  218  or analysis of medical sensor readings. 
     In the example of the above movie recommendation service, the information utility setting  222  can be a setting for the accuracy of recommendations generated based on the perturbed user information. In general, a higher degree of recommendation accuracy for the information utility setting  222  will constrain the generation of the perturbed user information  218  such that the differences between the perturbed user information  218  and the raw user information  210  are smaller relative to the differences when a lower degree of recommendation accuracy is configured for the information utility setting  222 . More specifically, the perturbed user information  218  can have a higher resemblance to the raw user information  210  for settings of higher recommendation accuracy of the information utility setting  222 . 
     In this implementation of the invention, the computing system  100  can generate the perturbed user information  218  on the first device  102 . More specifically, the perturbed user information  218  is generated solely on the first device  102 . 
     Referring now to  FIG. 3 , therein is shown an example of a display interface  330  of the first device  102  of  FIG. 1 . The display interface  330  depicts a privacy control interface  332 . The privacy control interface  332  is an interface that enables the user to select the privacy protection level for information associated with the user. More specifically, the privacy control interface  332  can enable the user to control the amount of privacy that is preserved when sharing the raw user information  210 , the perturbed user information  218  of  FIG. 2 , or a combination thereof with another device, such as the second device  106  of  FIG. 2 , for receiving recommendations based on the raw user information  210  of  FIG. 2 , the perturbed user information  218  of  FIG. 2 , or a combination thereof. 
     For illustrative purposes, the device interface  330  depicts the privacy control interface  332  for receiving a movie recommendation service, although it is understood that the privacy control interface  332  can be adapted for privacy control for other types of the raw user information  210 , such as medical records, online purchases, or locations visited. 
     In this example, the privacy control interface  332  can enable the user to select an information release setting  334  for the raw user information  210  related to providing recommendations of movie viewing. The privacy control interface  332  can provide an information release setting  334 , which is the level of security or privacy used to protect the raw user information. 
     The privacy release setting  334  can include the option for an overall privacy setting  336 , which is a general privacy setting that is applied to all of the raw user information  210  to be released from the first device  102 . Optionally, the privacy release setting  334  can include attribute based privacy settings  338 , which are privacy settings for individual instances of the information attributes  216 . For example, the user can have the option to set the attribute based privacy settings  338  at different levels for different instances of the attribute based privacy settings  338 . In another example, the overall privacy setting  336  can be selected in combination with the attribute based privacy settings  338 , which can differ or allow exceptions for specific instances of the information attributes  216 . 
     The level of privacy settings are determined by a privacy release policy  340  for the overall privacy setting  336 , the attribute based privacy settings  338  or a combination thereof. The privacy release policy  340  is the degree or levels of privacy that will be used to protect the information. The computing system  100  can generate the perturbed user information  218  from the raw user information  210  based on the privacy release policy  340 , which will be described in detail below. 
     As an example, the privacy release policy  340  can include the options of “full release,” “no release,” or “perturbed release.” The privacy release policy  340  of “full release” enables unmodified release of the raw user information  210 , or release of the raw user information  210  without the application of protective measures. The privacy release policy  340  of “perturbed release” enables the computing system  100  to release perturbed user information  218  based on the raw user information  210 . 
     The privacy release policy  340  of “no release” will prevent the computing system  100  from releasing the raw user information  210 . For example, when the privacy release policy  340  of “no release” is set as the attribute based privacy settings  338  for one or more instances of the information attributes  216 , the computing system  100  will prevent the release of any of the raw user information  210  that includes those particular instances of the information attributes  216 . 
     Optionally, the computing system  100  can present a protection failure notification  342 , such as a pop-up window, on the display interface  330 . For example, the protection failure notification  342  can include information stating that the computing system  100  cannot protect the raw user information  210  based on the privacy release policy  340  information release setting selected for overall privacy setting  336 , the attribute based privacy setting  338 , or a combination thereof. 
     Referring now to  FIG. 4 , therein is shown an exemplary block diagram of the computing system  100 . The computing system  100  can include the first device  102 , the communication path  104 , and the second device  106 . The first device  102  can send information in a first device transmission  408  over the communication path  104  to the second device  106 . The second device  106  can send information in a second device transmission  410  over the communication path  104  to the first device  102 . 
     For illustrative purposes, the computing system  100  is shown with the first device  102  as a client device, although it is understood that the computing system  100  can have the first device  102  as a different type of device. For example, the first device  102  can be a server having a display interface. 
     Also for illustrative purposes, the computing system  100  is shown with the second device  106  as a server, although it is understood that the computing system  100  can have the second device  106  as a different type of device. For example, the second device  106  can be a client device. 
     For brevity of description in this embodiment of the present invention, the first device  102  will be described as a client device and the second device  106  will be described as a server device. The embodiment of the present invention is not limited to this selection for the type of devices. The selection is an example of an embodiment of the present invention. 
     The first device  102  can include a first control unit  412 , a first storage unit  414 , a first communication unit  416 , and a first user interface  418 . The first control unit  412  can include a first control interface  422 . The first control unit  412  can execute a first software  426  to provide the intelligence of the computing system  100 . 
     The first control unit  412  can be implemented in a number of different manners. For example, the first control unit  412  can be a processor, an application specific integrated circuit (ASIC), an embedded processor, a microprocessor, a hardware control logic, a hardware finite state machine (FSM), a digital signal processor (DSP), or a combination thereof. The first control interface  422  can be used for communication between the first control unit  412  and other functional units in the first device  102 . The first control interface  422  can also be used for communication that is external to the first device  102 . 
     The first control interface  422  can receive information from the other functional units or from external sources, or can transmit information to the other functional units or to external destinations. The external sources and the external destinations refer to sources and destinations external to the first device  102 . 
     The first control interface  422  can be implemented in different ways and can include different implementations depending on which functional units or external units are being interfaced with the first control interface  422 . For example, the first control interface  422  can be implemented with a pressure sensor, an inertial sensor, a microelectromechanical system (MEMS), optical circuitry, waveguides, wireless circuitry, wireline circuitry, or a combination thereof. 
     The first storage unit  414  can store the first software  426 . The first storage unit  414  can also store the relevant information, such as the raw user information  210  of  FIG. 2 , the overall privacy setting  336  of  FIG. 3  for information release setting  334  of  FIG. 3  associated information attributes  216  of  FIG. 2 , or a combination thereof. 
     The first storage unit  414  can be a volatile memory, a nonvolatile memory, an internal memory, an external memory, or a combination thereof. For example, the first storage unit  414  can be a nonvolatile storage such as non-volatile random access memory (NVRAM), Flash memory, disk storage, or a volatile storage such as static random access memory (SRAM). 
     The first storage unit  414  can include a first storage interface  424 . The first storage interface  424  can be used for communication between and other functional units in the first device  102 . The first storage interface  424  can also be used for communication that is external to the first device  102 . 
     The first storage interface  424  can receive information from the other functional units or from external sources, or can transmit information to the other functional units or to external destinations. The external sources and the external destinations refer to sources and destinations external to the first device  102 . 
     The first storage interface  424  can include different implementations depending on which functional units or external units are being interfaced with the first storage unit  414 . The first storage interface  424  can be implemented with technologies and techniques similar to the implementation of the first control interface  422 . 
     The first communication unit  416  can enable external communication to and from the first device  102 . For example, the first communication unit  416  can permit the first device  102  to communicate with the second device  106  of  FIG. 1 , an attachment, such as a peripheral device or a computer desktop, and the communication path  104 . 
     The first communication unit  416  can also function as a communication hub allowing the first device  102  to function as part of the communication path  104  and not limited to be an end point or terminal unit to the communication path  104 . The first communication unit  416  can include active and passive components, such as microelectronics or an antenna, for interaction with the communication path  104 . 
     The first communication unit  416  can include a first communication interface  428 . The first communication interface  428  can be used for communication between the first communication unit  416  and other functional units in the first device  102 . The first communication interface  428  can receive information from the other functional units or can transmit information to the other functional units. 
     The first communication interface  428  can include different implementations depending on which functional units are being interfaced with the first communication unit  416 . The first communication interface  428  can be implemented with technologies and techniques similar to the implementation of the first control interface  422 . 
     The first user interface  418  allows a user (not shown) to interface and interact with the first device  102 . The first user interface  418  can include an input device and an output device. Examples of the input device of the first user interface  418  can include a keypad, a touchpad, soft-keys, a keyboard, a microphone, an infrared sensor for receiving remote signals, or any combination thereof to provide data and communication inputs. 
     The first user interface  418  can include a first display interface  430 . The first display interface  430  can include a display, a projector, a video screen, a speaker, or any combination thereof. 
     The first control unit  412  can operate the first user interface  418  to display information generated by the computing system  100 . The first control unit  412  can also execute the first software  426  for the other functions of the computing system  100 . The first control unit  412  can further execute the first software  426  for interaction with the communication path  104  via the first communication unit  416 . 
     The second device  106  can be optimized for implementing an embodiment of the present invention in a multiple device embodiment with the first device  102 . The second device  106  can provide the additional or higher performance processing power compared to the first device  102 . The second device  106  can include a second control unit  434 , a second communication unit  436 , and a second user interface  438 . 
     The second user interface  438  allows a user (not shown) to interface and interact with the second device  106 . The second user interface  438  can include an input device and an output device. Examples of the input device of the second user interface  438  can include a keypad, a touchpad, soft-keys, a keyboard, a microphone, or any combination thereof to provide data and communication inputs. Examples of the output device of the second user interface  438  can include a second display interface  440 . The second display interface  440  can include a display, a projector, a video screen, a speaker, or any combination thereof. 
     The second control unit  434  can execute a second software  442  to provide the intelligence of the second device  106  of the computing system  100 . The second software  442  can operate in conjunction with the first software  426 . The second control unit  434  can provide additional performance compared to the first control unit  412 . 
     The second control unit  434  can operate the second user interface  438  to display information. The second control unit  434  can also execute the second software  442  for the other functions of the computing system  100 , including operating the second communication unit  436  to communicate with the first device  102  over the communication path  104 . 
     The second control unit  434  can be implemented in a number of different manners. For example, the second control unit  434  can be a processor, an embedded processor, a microprocessor, hardware control logic, a hardware finite state machine (FSM), a digital signal processor (DSP), or a combination thereof. 
     The second control unit  434  can include a second controller interface  444 . The second controller interface  444  can be used for communication between the second control unit  434  and other functional units in the second device  106 . The second controller interface  444  can also be used for communication that is external to the second device  106 . 
     The second controller interface  444  can receive information from the other functional units or from external sources, or can transmit information to the other functional units or to external destinations. The external sources and the external destinations refer to sources and destinations external to the second device  106 . 
     The second controller interface  444  can be implemented in different ways and can include different implementations depending on which functional units or external units are being interfaced with the second controller interface  444 . For example, the second controller interface  444  can be implemented with a pressure sensor, an inertial sensor, a microelectromechanical system (MEMS), optical circuitry, waveguides, wireless circuitry, wireline circuitry, or a combination thereof. 
     The second storage unit  446  can store the second software  442 . For example, the second storage unit  446  can also store the relevant information, such as the raw user information  210  of  FIG. 2 . 
     For illustrative purposes, the second storage unit  446  is shown as a single element, although it is understood that the second storage unit  446  can be a distribution of storage elements. Also for illustrative purposes, the computing system  100  is shown with the second storage unit  446  as a single hierarchy storage system, although it is understood that the computing system  100  can have the second storage unit  446  in a different configuration. For example, the second storage unit  446  can be formed with different storage technologies forming a memory hierarchal system including different levels of caching, main memory, rotating media, or off-line storage. 
     The second storage unit  446  can be a volatile memory, a nonvolatile memory, an internal memory, an external memory, or a combination thereof. For example, the second storage unit  446  can be a nonvolatile storage such as non-volatile random access memory (NVRAM), Flash memory, disk storage, or a volatile storage such as static random access memory (SRAM). 
     The second storage unit  446  can include a second storage interface  448 . The second storage interface  448  can be used for communication between other functional units in the second device  106 . The second storage interface  448  can also be used for communication that is external to the second device  106 . 
     The second storage interface  448  can receive information from the other functional units or from external sources, or can transmit information to the other functional units or to external destinations. The external sources and the external destinations refer to sources and destinations external to the second device  106 . 
     The second storage interface  448  can include different implementations depending on which functional units or external units are being interfaced with the second storage unit  446 . The second storage interface  448  can be implemented with technologies and techniques similar to the implementation of the second controller interface  444 . 
     The second communication unit  436  can enable external communication to and from the second device  106 . For example, the second communication unit  436  can permit the second device  106  to communicate with the first device  102  over the communication path  104 . 
     The second communication unit  436  can also function as a communication hub allowing the second device  106  to function as part of the communication path  104  and not limited to be an end point or terminal unit to the communication path  104 . The second communication unit  436  can include active and passive components, such as microelectronics or an antenna, for interaction with the communication path  104 . 
     The second communication unit  436  can include a second communication interface  450 . The second communication interface  450  can be used for communication between the second communication unit  436  and other functional units in the second device  106 . The second communication interface  450  can receive information from the other functional units or can transmit information to the other functional units. 
     The second communication interface  450  can include different implementations depending on which functional units are being interfaced with the second communication unit  436 . The second communication interface  450  can be implemented with technologies and techniques similar to the implementation of the second controller interface  444 . 
     The first communication unit  416  can couple with the communication path  104  to send information to the second device  106  in the first device transmission  408 . The second device  106  can receive information in the second communication unit  436  from the first device transmission  408  of the communication path  104 . 
     The second communication unit  436  can couple with the communication path  104  to send information to the first device  102  in the second device transmission  410 . The first device  102  can receive information in the first communication unit  416  from the second device transmission  410  of the communication path  104 . The computing system  100  can be executed by the first control unit  412 , the second control unit  434 , or a combination thereof. For illustrative purposes, the second device  106  is shown with the partition having the second user interface  438 , the second storage unit  446 , the second control unit  434 , and the second communication unit  436 , although it is understood that the second device  106  can have a different partition. For example, the second software  442  can be partitioned differently such that some or all of its function can be in the second control unit  434  and the second communication unit  436 . Also, the second device  106  can include other functional units not shown in  FIG. 4  for clarity. 
     The functional units in the first device  102  can work individually and independently of the other functional units. The first device  102  can work individually and independently from the second device  106  and the communication path  104 . 
     The functional units in the second device  106  can work individually and independently of the other functional units. The second device  106  can work individually and independently from the first device  102  and the communication path  104 . 
     For illustrative purposes, the computing system  100  is described by operation of the first device  102  and the second device  106 . It is understood that the first device  102  and the second device  106  can operate any of the functions of the computing system  100 . 
     Referring now to  FIG. 5 , therein is shown an exemplary architecture diagram for the computing system  100  of  FIG. 1 . The architecture diagram depicts the system components for generating the perturbed user information  218  in an information perturbation block  502 . For example, inputs to the information perturbation block  502  can include: the raw user information  210 , a user category factor  510 , a public category factor  512 , a privacy notion  554  based on the information release setting  334 , an information utility notion  556 , or a combination thereof. The inputs to the information perturbation block  502  will be discussed in detail below. 
     As a further example, the components and functions of the information perturbation block  510  for generating the perturbed user information  218  can include: an attribute aggregate  574  in an attribute aggregate array  572 ; a category sum  580  and information category aggregate  582 , both in a category count array  578 ; a sensitivity factor  562 , an admissible noise  560 ; a noise adjusted aggregate  592  in a noise adjusted attribute array  590 ; an augmented lagrangian function  514 , or a combination thereof. The details for implementation of the components and functions of the architecture diagram for generating the perturbed user information  218  will be described below. 
     Referring now to  FIG. 6 , therein is shown an exemplary flow chart  600  representing the operation of the computing system  100  of  FIG. 1 . The computing system  100  can utilize one or more of the user interfaces, communication circuits, control circuits, location circuits, storage circuits, or a combination thereof as illustrated in  FIG. 4  and described above to implement one or more functions, instructions, steps, or a combination thereof described below. 
     For example, the computing system  100  can use the first user interface  418  of  FIG. 4 , the second user interface  438  of  FIG. 4 , the first communication circuit  416  of  FIG. 4 , the second communication circuit  436  of  FIG. 4 , the first control circuit  412  of  FIG. 4 , the second control circuit  434  of  FIG. 4 , the first storage unit  414  of  FIG. 4 , the second storage unit  446  of  FIG. 4 , or a combination thereof to implement the first software  426  of  FIG. 4 , the second software  442  of  FIG. 4 , or a combination thereof. The first software  426 , the second software  442 , or a combination thereof can include the functions, the instructions, the steps, or a combination thereof described below. 
     The computing system  100  can perform the functions, the instructions, the steps, or a combination thereof for determining information format, determining user requirements, determining a privacy notion, determining an information utility notion, performing information perturbation, and transmitting user information. 
     An information format determination step  640  is for determining the information format  220  of  FIG. 2  for the raw user information  210  of  FIG. 2 . For example, the computing system  100  can determine the information format  220  based on the information classification  212  of  FIG. 2  for the raw user information  210  and the attribute category  214  of  FIG. 2  for the information attributes  216  of  FIG. 2 . In one example, the information format  220  can be given as a template with the information classification  212  and the attribute category  214  provided. In another example, the first control unit  412  can parse the raw user information  210  to determine the information classification  212  based on similarities between instances of the raw user information  210 . Similarly, the first control unit  412  can parse the information attributes  216  associated with each of the raw user information  210  to determine the attribute category  214 . 
     A user requirement determination step  642  is for determining the preferences for protection of the raw user information  210 . In the user requirement determination step  642 , the computing system  100  can obtain the information release setting  334  of  FIG. 3  for the raw user information  210 , which can include the overall privacy setting  336  of  FIG. 3 , the categorical privacy setting  338  of  FIG. 3 , or a combination thereof. In one example, the user can input the information release setting  334  for the raw user information  210  through the privacy control interface  332  of  FIG. 2 , which can be displayed on the first user interface  418  of  FIG. 4 . In another example, the first control unit  412  can communicate with the first storage unit  414  of  FIG. 4 , the second storage unit  446  of  FIG. 4 , or a combination thereof to obtain the information release setting  334  that have been previously stored. As a specific example, the information release setting  334  for the raw user information  210  that have been previously stored can include a system default or factory setting, such as a non-alterable setting for the information release setting  334 . Optionally, the computing system  100  can also obtain the information utility setting  222  of  FIG. 2  through the first user interface  418  or the first storage unit  414 . 
     A privacy notion determination step  644  is for determining the privacy notion  554  of  FIG. 5  associated with the information release setting  334 . The privacy notion  554  is the mechanism or model applied to the raw user information  210  for generating the perturbed user information  218  of  FIG. 2 . For example, the first control unit  412  can determine the privacy notion  554  as differential privacy based method, information gain based method, or mutual information based method 
     An information utility notion determination step  646  is for determining the information utility notion  556  of  FIG. 5  based on the user input or system default and factors associated with the raw user information  210  as indicated by the information utility setting  222 . The information utility notion  556  can be constraints on generation of the perturbed user information  218  for the purpose of setting the range of accuracy for analysis of the perturbed user information  218  relative to the raw user information  210 . For example, the information utility notion  556  can include methods based on collaborative filtering, root mean square error, mean absolute error, or top-k recommendation. As an example, the first control unit  412  can determine the information utility notion  556  based on analysis of factors associated with the raw user information  210 , such as metadata and histograms. Determination of the information utility notion  556  can be optional. 
     An information perturbation performance step  650  is for generating the perturbed user information  218  from the raw user information  210 . In general, the information perturbation step  650  can include a noise calibration phase and an information sanitization phase, which will be discussed in detail below. 
     The computing system  100  can iterate through the information perturbation step  650  to determine convergence for the perturbed user information  218  with the first control unit  412 . When convergence is determined, the first control unit  412  can generate the perturbed user information  218  and communicate with the first display interface  330  of  FIG. 4  for displaying the perturbed user information  218 . Alternatively, when the computing system  100  determines that no solution exists due to non-convergence, the computing system  100  can provide a notification, such as the protection failure notification  342  of  FIG. 3  to the user indicating that the perturbed user information  218  could not be generated based on the information release setting  334 , the information utility setting  222  or a combination thereof. The first control unit  412  can also communicate with the first storage unit  414  to store the perturbed user information  218  for subsequent processing. 
     The information transmission step  652  is for transmission of the perturbed user information  218 . In the information transmission step  652 , the computing system  100  can transmit the perturbed user information  218  to a device or entity external to the first device  102 . For example, the computing system  100  can interface with the first communication unit  416  of  FIG. 4  to transmit the perturbed user information  218  to the second device  106  of  FIG. 1  for processing of the perturbed user information  218 . As a specific example, the first control unit  412  can interface with the first communication unit  416  of  FIG. 4  to transmit the perturbed user information  218  related to movies to the recommendation system of the second device  106 , as illustrated in  FIG. 2 . 
     The steps described in this disclosure can be implemented as instructions stored on a non-transitory computer readable medium to be executed by a first control unit  412 , the second control unit  436 , or a combination thereof. The non-transitory computer medium can include the first storage unit  414  of  FIG. 4 , the second storage unit  446  of  FIG. 4 , or a combination thereof. The non-transitory computer readable medium can include non-volatile memory, such as a hard disk drive, non-volatile random access memory (NVRAM), solid-state storage device (SSD), compact disk (CD), digital video disk (DVD), or universal serial bus (USB) flash memory devices. The non-transitory computer readable medium can be integrated as a part of the computing system  100  or installed as a removable portion of the computing system  100 . 
     The physical transformation from receiving recommendations based on the perturbed user information  218  results in the movement in the physical world, such as the user interacting with the first device  102  to make a selection based on the recommendation. Movement in the physical world results in generation of new instances of the raw user information  210 . 
     Referring now to  FIG. 7 , therein is shown the information perturbation step  650  of  FIG. 6 . As described above, the first control unit  412  can generate the perturbed user information  218  of  FIG. 2  from the raw user information  210  of  FIG. 2 . As an example, this implementation of the information perturbation step  650  is based on the privacy notion  554  of  FIG. 5  of differential privacy. However, it is understood that the information perturbation step  650  can generate the perturbed user information  218  based on different methods of the privacy notion  554 , such as mutual information. 
     In general, the information perturbation step  650  can include a noise calibration phase and an information sanitization phase. The noise calibration phase is for determining the magnitude of the admissible noise  560  of  FIG. 5  for the perturbed user information  218 . The admissible noise  560  can be calculated with an admissible noise function according to Equation 1 as follows:
 
 NC ( D   r )= f ( D   r )+Δ f   L *( D   r ,β) Z/α   Eq. 1
 
     Where NC(D r ) represents the admissible noise  560 , D r  is a value associated with the raw user information  210 , Δf L *(D r ,β) represents the sensitivity factor  562  of  FIG. 5 , Z represents the noise distribution factor, and α and β represents noise distribution modifiers. For example, the noise distribution modifiers can be determined as privacy budgets based on the information release setting  334  of  FIG. 3 . The sensitivity factor  562  can be based on the difference between an adjacent pair of information in a set of information. 
     In the noise calibration phase, the first control unit  412  of  FIG. 4  can calculate the admissible noise  560  according to Eq. 1 above in an attribute aggregation step  764 , a category counting step  768 , a sensitivity determination step  770 , and a noise addition step  771 . 
     The attribute aggregation step  764  is for generating the attribute aggregate array  572  of  FIG. 5 . The attribute aggregate array  572  is an array that contains correlations between an instance of the information attribute  216  of  FIG. 2  and the number of the raw user information  210  of  FIG. 2  that is associated with the particular instance of the information attribute  216 . For example, an entry in the attribute aggregate array  572  can include the attribute aggregate  574  of  FIG. 5  for each of the information attributes  216  that appear in the set of the information attribute  216 . 
     The attribute aggregate  574  is the number of times that the raw user information  210  includes a particular instance of the information attributes  216 . For example, if three of the movies include the information attributes  216  of “action,” then the attribute aggregate  574  corresponding with “action” can be determined as 3. The first control unit  412  can iterate through each instance of the raw user information  210  to generate the attribute aggregate array  572 . 
     The category count step  768  is for generating the category count array  578  of  FIG. 5 . The category count array  578  is an array that contains correlations between the total number of instances that each of a particular type of the information attributes  216  is correlated with one of the raw user information  210 . For example, each entry in the category count array  578  can include the information category aggregate  582  of  FIG. 5  associated with the category sum  580  of  FIG. 5 . 
     The category sum  580  is the total number of the information attributes  216  that are associated with a particular instance of the raw user information  210 . As an example, the first control unit  412  can calculate the category sum  580  as “three” for one of the raw user information  210  that include three different or unique instances of the information attributes  216 . As a specific example, referring to TABLE 1 of  FIG. 2 , the category sum  580  for the raw user information  210  of “Godzilla” can be calculate by the first control unit  412  as “three” since “Godzilla” includes the information attributes  216  of “action,” “sci-fi,” and “thriller.” 
     The information category aggregate  582  is the number of the raw user information  210  that matches a particular instance of the category sum  580 . For example, referring again to TABLE 1 of  FIG. 2 , since two movies, “Godzilla” and “The Amazing Spider-Man 2” each includes three of the information attributes  216 , the first control unit  412  can calculate the information category aggregate  582  as “two” for the category sum  580  having the value of “three.” The first control unit  412  can generate the category count array  578  through iteration of the raw user information  210 . 
     The first control unit  412  can calculate the sensitivity factor  562  based on the category count array  578 . For example, in the sensitivity determination step  770 , the first control unit  412  can calculate the sensitivity factor  562  based on a sensitivity function according to Equation 2 as follows:
 
Δ f   L *( D   r ,β)=max D ,(Δ f   L ( D ′)*exp(−β* d ( D,D ′)))  Eq. 2
 
     Where D′ represents the category sum  580  and D′ represents the information category aggregate  582 , both of the category count array  578 . 
     The first control unit  412  can determine the sensitivity factor  562  based on iteration through the category count array  578 . For example, the first control unit  412  can determine the sensitivity factor  562  as the maximum value amongst those calculated with the category count array  578 . 
     The noise addition step  771  is for calculating noise adjusted values as precursors for generating the perturbed user information  218 . In the noise addition step  771 , the first control unit  412  can generate the noise adjusted attribute array  590  of  FIG. 5  from the attribute aggregate array  572  and the sensitivity factor  562  with Equation 1, above. The noise adjusted attribute array  590  can include the noise adjusted aggregate  592  of  FIG. 5 , which are the attribute aggregate  574  that have been adjusted based on the admissible noise  560 . The noise adjusted aggregate  592  can be further modified in the information sanitization phase to generate the perturbed user information  218 . In addition, the noise addition step  771  can calculate the admissible noise  560  according to Equation 1 with the values determined for the sensitivity factor  562 . 
     The information sanitization phase can be implemented to generate the perturbed user information  218  based on the admissible noise  560  calculated in the noise calibration phase. In the information sanitization phase, the computing system  100  can generate the perturbed user information  218  with a constraint relaxation step  794 , a category optimization step  796 , and an information rounding step  798 . 
     In general, the perturbed user information  218  can be generated based on values that provide the maximum distance from the raw user information  210  in accordance with a user privacy concern based on the information release settings of  FIG. 3 . This relationship can be illustrated in Equation 3 as follows: 
     
       
         
           
             
               
                 
                   max 
                   ⁢ 
                   
                     1 
                     2 
                   
                   ⁢ 
                   
                     
                        
                       
                         W 
                         ⁡ 
                         
                           ( 
                           
                             
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                   . 
                   
                       
                   
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                   3 
                 
               
             
           
         
       
     
     Where “W” represents the user privacy concern factor, “d r ” represents the perturbed user information  218 , and “d r ” represents the raw user information  210 . As an example, the user privacy concern factor can be a matrix with weights that can be applied to the raw user information  210 . 
     In addition, calculation of the perturbed user information  218  can include the constraints of Equation 4 and Equation 5, below:
 
   NC −v   l   ≤C   T   d   p   ≤ NC +v   r   Eq. 4
 
     Equation 4 represents the constraints based on the admissible noise  560 , NC, where v l  and V r  represent noise parametric factors,  NC  represents the average admissible noise  560  as calculated in the noise calibration phase above, and C represents the public category factor  512  of  FIG. 5 . The public category factor  512  is a representation of the publicly available information for determining the degree of similarity between the perturbed user information  218  and the public information.
 
 d   p   =Cd   c ∈{0,1} n   Eq. 5
 
     Equation 5 provides a definition for the perturbed user information  218  based on a collaborative filtering matrix factorization, where d c  represents the user category factor  510  of  FIG. 5 . The user category factor  510  is a factor that representations the different types of the information attributes  216  for the set of the raw user information  210 . 
     It has been discovered that the method of collaborative filtering based on matrix factorization can be implemented in the absence of additional user information based on the public category factor  512  and the user category factor  510 . Since the latent factors for matrix factorization correlate closely with category information, such as the public category factor  512  and the user category factor  510 , the computing system  100  can generate the perturbed user information  218  to resemble public information based on the product of the public category factor  512  and the user category factor  510 . 
     The constraint relaxation step  794  is for relaxing the constraints associated for calculation of the optimum values for the perturbed user information  218 . In the constraint relaxation step  794 , the first control unit  412  can relax the constraints of equation 4 and 5 above to calculate the user category factor  510 . For example, the constraints for the noise parametric factors, v l  and v r , can be redefined according to Equation 6 as follows: 
     
       
         
           
             
               
                 
                   
                     
                        
                       
                         
                           v 
                           l 
                         
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                     ⁢ 
                     
                         
                     
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                   6 
                 
               
             
           
         
       
     
     Where “γ” represents the utility notion constraint associated with the information utility notion  556  of  FIG. 5  and Δf L * represents the sensitivity factor  562  of equation 1. The constraint based on the admissible noise  560  of Eq. 4 above can be relaxed according to Equation 6 as follows:
 
   NC −v   l   ≤C   T   C{circumflex over (d)}   c   ≤ NC +v   r   Eq. 7
 
     Where {circumflex over (d)} c  represents fractional values of the user category factor  510 : 0≤C{circumflex over (d)} c ≤1. 
     Based on the relaxed constraints, Equation 3 can be redefined according to Equation 8 as follows: 
     
       
         
           
             
               
                 
                   max 
                   ⁢ 
                   
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     Based on the relaxed function of Equation 8, the first control unit  412  can proceed to the category optimization step  796  to generate the perturbed user information  218 . 
     The category optimization step  796  is for generating the perturbed user information  218  based on the optimal value of the user category factor  510 . The first control unit  412  can implement an iterative learning function to calculate the user category factor  510  based on relaxed constraints as defined by Equations 6-8 above. As an example the iterative learning function can be based on the augmented Lagrangian function  514  of  FIG. 5  in Equation 9 as follows: 
     
       
         
           
             
               
                 
                   
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     Where “y” represents the Lagrange multiplier estimate, “h t ” represents the Lagrange constraint functions, and “u” is the initial estimate for the Lagrange function. The Lagrange constraint functions can be based on slack variables “x i ” for each of the above relaxed constraints of Equations 6 and 7. The Lagrange constraint functions can be defined by the following equations: 
     
       
         
           
             
               
                 
                   
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                   14 
                 
               
             
           
         
       
     
     The first control unit  412  can implement the augmented Lagrangian function  514  for a predetermined number of iterations for each of the Lagrange constraint functions. Initially, the variables {circumflex over (d)} c , v l , V r , x i  can be randomly selected. For each subsequent iteration, the first control unit  412  can check for convergence of the augmented Lagrangian function  514 . Convergence of the augmented Lagrangian function provides the perturbed user information  218  having the maximum distance from the raw user information  210 . Once convergence has been reached, the first control unit  412  can determine the solution as the user category factor  510 . 
     If no convergence has been reached, the first control unit  412  can update the values of {circumflex over (d)} c , v l , v r , x i  can be updated based on a gradient descent method and the Lagrange multiplier estimate “y” can be updated based on the Lagrange constraint functions. 
     After the predetermined number of iterations, if no convergence is reached, the first control unit  412  can determine that generation of the perturbed user information  218  is infeasible and the first control unit  412  can generate the notification stating that a solution is not possible with the information release setting  334 , and information utility setting  222 , or a combination thereof. 
     The information rounding step  798  is for rounding the fractional values of the user category factor  510  calculated in the category optimization step  796 . For example, in the information rounding step  798 , the first control unit  412  can implement a probabilistic approach to round the user category factor  510  to generate the perturbed user information  218  based on feasibility according to the noise parametric factors v l  and V r . In general, the first control unit  412  can round the fractional values of the user category factor  510  to the next whole number, such as to a value of “one” when the user category factor  510  value is greater than or equal to “one half”. However, if rounding of the user category factor  510  value is not feasible, the first control unit  412  can minimize the infeasibility for the distance between v l  and v r . The distance between can be minimized by a function such as a greedy flip function. 
     It has been discovered that generating the perturbed user information  218  from the raw user information  210  eliminates the need for encryption or other security measure to protect the raw user information  210 . The perturbed user information  218  has been generated to be maximally different from the raw user information  210  but preserve sufficient correlation to provide accurate recommendations and analysis based on the privacy release policy  340 , which eliminates the need for additional security or protective measures, such as encryption. 
     Referring now to  FIG. 8 , therein is shown a flow chart of a method  800  of operation of a computing system  100  of  FIG. 1  in a further embodiment. The exemplary flow chart  800  can include obtaining an information release setting for raw user information, the raw user information including an information attribute in a step  802 . The computing system  100  can receive the information release setting of  FIG. 3  as described above in the determining user requirements step  642  of  FIG. 6 . 
     Obtaining an information release setting can further include receiving an attribute based privacy settings for the information attribute of the raw user information in a step  810 . The computing system  100  can receive the attribute based privacy settings  338  of  FIG. 3  as described above in the determining user requirements step  642  of  FIG. 6 . 
     The exemplary flow chart  800  can include determining an information format for the information attribute of the raw user information in step  804 . The computing system  100  can determine the information format  220  of  FIG. 2  as described above the determining information format step  640  of  FIG. 6 . 
     The exemplary flow chart  800  can include determining a privacy notion based on the privacy setting in a step  806 . The computing system  100  can determine the privacy notion  554  of  FIG. 6  as described above in the determining privacy notion step  644 . 
     The exemplary flow chart  800  can include generating perturbed user information from the information attribute based on the privacy notion, wherein the information format for the raw user information is preserved in the perturbed user information in step  808 . The computing system  100  can generate the perturbed user information  218  of  FIG. 2  as described above in the information perturbation step  650  of  FIG. 6 . 
     Generating perturbed user information can further include generating the perturbed user information to resemble public information based on a public category factor in step  812 . Further, generating perturbed user information can include generating the perturbed user information based on an information utility notion for recommendation accuracy of the raw user information in step  814 . Yet further, generating perturbed user information can further include generating the perturbed user information based on addition of admissible noise with a sensitivity factor in step  816 . 
     The resulting method, process, apparatus, device, product, and/or system is straightforward, cost-effective, uncomplicated, highly versatile, accurate, sensitive, and effective, and can be implemented by adapting known components for ready, efficient, and economical manufacturing, application, and utilization. Another important aspect of an embodiment of the present invention is that it valuably supports and services the historical trend of reducing costs, simplifying systems, and increasing performance. 
     These and other valuable aspects of an embodiment of the present invention consequently further the state of the technology to at least the next level. 
     While the invention has been described in conjunction with a specific best mode, it is to be understood that many alternatives, modifications, and variations will be apparent to those skilled in the art in light of the aforegoing description. Accordingly, it is intended to embrace all such alternatives, modifications, and variations that fall within the scope of the included claims. All matters set forth herein or shown in the accompanying drawings are to be interpreted in an illustrative and non-limiting sense.