Patent Publication Number: US-11036886-B2

Title: Iterative execution of data de-identification processes

Description:
BACKGROUND 
     1. Technical Field 
     Present invention embodiments relate to data de-identification, and more specifically, to multiple different de-identification processes iteratively executing to de-identify data. 
     2. Discussion of the Related Art 
     Data de-identification or anonymization refers to the practice of transforming features in data in order to prevent a person&#39;s identity from being linked to other information about the person. Data is commonly de-identified in order to make use of the data while preserving the privacy of individuals. For example, medical data may be de-identified before it is used for clinical research purposes. Data utility, or usefulness of the data, generally decreases as that data is further de-identified. Thus, there is typically a balance between data privacy and data utility. 
     Data owners are increasingly interested in knowing how their data is being transformed in order to meet de-identification or anonymization requirements. Furthermore, data owners are interested in knowing which algorithms best strike a balance between ensuring de-identification or anonymization, while also preserving data utility. 
     SUMMARY 
     According to one embodiment of the present invention, a computer system de-identifies data by selecting one or more attributes of a dataset and determining a set of data de-identification techniques associated with each attribute. Each de-identification technique is evaluated with respect to an impact on data privacy and an impact on data utility based on a series of metrics, and a data de-identification technique is recommended for each attribute based on the evaluation. The dataset is de-identified by applying the de-identification technique that is recommended for each attribute. Embodiments of the present invention further include a method and program product for de-identifying data in substantially the same manner described above. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       Generally, like reference numerals in the various figures are utilized to designate like components. 
         FIG. 1  is a block diagram depicting a computing environment for data de-identification in accordance with an embodiment of the present invention; 
         FIG. 2  is a flow chart depicting a method of de-identifying data in accordance with an embodiment of the present invention; 
         FIGS. 3A and 3B  illustrate examples of a dataset for de-identification in accordance with an embodiment of the present invention; 
         FIG. 4A  illustrates an example of a de-identification algorithm evaluation table in accordance with an embodiment of the present invention 
         FIG. 4B  illustrates an example of a single de-identification provider evaluation dashboard in accordance with an embodiment of the present invention; 
         FIG. 4C  illustrates an example of a multiple de-identification provider evaluation dashboard in accordance with an embodiment of the present invention; and 
         FIG. 5  is a block diagram depicting a computing device in accordance with an embodiment of the present invention. 
     
    
    
     DETAILED DESCRIPTION 
     Present invention embodiments relate generally to data de-identification, and more specifically, to optimizing the data de-identification process by executing multiple different de-identification processes to iteratively de-identify data. Personal data, or personally identifiable information (PII), includes data that potentially identifies a specific person or entity. Data de-identification involves transforming identifying features, such as direct identifiers and quasi-identifiers, in a dataset so that they can no longer be used for re-identification attacks. Direct identifiers may immediately identify individuals without requiring any other information. For example, direct identifiers may include a full name, social security number, or address. Quasi-identifiers are data attributes that, unlike direct identifiers, cannot be used alone to re-identify individuals. However, when quasi-identifier attributes are used together, the combinations of values of the quasi-identifier attributes in the dataset may be unique for some individuals, leading to their re-identification. For example, the combination of values for attributes “five-digit zip code”, “gender”, and “date of birth” is unique for a large number of United States citizens, hence these attributes may be used together to issue successful re-identification attacks. 
     Due to various privacy regulations and other requirements there are many different approaches to de-identifying data to facilitate secondary uses. However, as a dataset&#39;s degree of de-identification increases, its utility decreases. Embodiments of the present invention evaluate a dataset in order to apply de-identification techniques that offer the highest level of privacy protection at the least expense to data utility. By iterating this process with different de-identification processes, a dataset may achieve a high level of anonymization while preserving its utility. Embodiments of the present invention efficiently achieve de-identification (since de-identification techniques are selected based on their evaluation/score and applied successively), thereby saving computing resources such as processing power and memory by quickly arriving at the de-identification results that are desired while preserving data utility. 
     It should be noted that references throughout this specification to features, advantages, or similar language herein do not imply that all of the features and advantages that may be realized with the embodiments disclosed herein should be, or are in, any single embodiment of the invention. Rather, language referring to the features and advantages is understood to mean that a specific feature, advantage, or characteristic described in connection with an embodiment is included in at least one embodiment of the present invention. Thus, discussions of the features, advantages, and similar language, throughout this specification may, but do not necessarily, refer to the same embodiment. 
     Furthermore, the described features, advantages, and characteristics of the invention may be combined in any suitable manner in one or more embodiments. One skilled in the relevant art will recognize that the invention may be practiced without one or more of the specific features or advantages of a particular embodiment. In other instances, additional features and advantages may be recognized in certain embodiments that may not be present in all embodiments of the invention. 
     These features and advantages will become more fully apparent from the following drawings, description and appended claims, or may be learned by the practice of embodiments of the invention as set forth hereinafter. 
     Present invention embodiments will now be described in detail with reference to the Figures.  FIG. 1  is a block diagram depicting a computing environment  100  for data de-identification in accordance with an embodiment of the present invention. As depicted, computing environment  100  includes user device  110 , database  120 , network  130 , server  140 , vulnerability analysis module  150 , provider evaluation module  160 , de-identification module  170 , and storage  180 . 
     User device  110  may include a laptop computer, a tablet computer, a netbook computer, a personal computer (PC), a desktop computer, a personal digital assistant (PDA), a smart phone, a thin client, or any programmable electronic device capable of executing computer readable program instructions. A user of user device  110  may provide initial datasets to be de-identified to database  120  or server  140 . A user may also interact with de-identified data using device  110 . In some embodiments, the user of user device  110  is the owner of one or more datasets that are de-identified according to embodiments of the subject invention. User device  110  may include internal and external hardware components, as depicted and described in further detail with respect to  FIG. 5 . In some embodiments, a data owner may interact with server  140  using user device  110  to view evaluations of de-identification providers and to make selections regarding the de-identification process. 
     Database  120  may include any non-volatile storage media known in the art. For example, database  120  can be implemented with a tape library, optical library, one or more independent hard disk drives, or multiple hard disk drives in a redundant array of independent disks (RAID). Similarly, data on database  120  may conform to any suitable storage architecture known in the art, such as a file, a relational database, an object-oriented database, and/or one or more tables. 
     Database  120  may include data that is considered publicly-available and/or private (e.g., proprietary data belonging to one or more data owners). Data in database  120  may include data that has not yet been de-identified or data that has been de-identified according to one or several standards. A user (such as a data owner) may interact with data stored on database  120  via user device  110 . 
     Network  130  may include a local area network (LAN), a wide area network (WAN) such as the Internet, or a combination of the two, and include wired, wireless, or fiber optic connections. In general, network  130  can be any combination of connections and protocols that will support communications between user device  110 , database  120 , and server  140  in accordance with embodiments of the present invention. 
     Server  140  may include vulnerability analysis module  150 , provider evaluation module  160 , de-identification module  170 , and storage  180 . In general, server  140  and its modules may select attributes in a dataset by performing a vulnerability analysis, select de-identification providers for each attribute, evaluate the selected providers with respect to their degree of de-identification and effect on data utility, and de-identify the dataset using one or more providers. Server  140  may include internal and external hardware components, as depicted and described in further detail with respect to  FIG. 5 . 
     Vulnerability analysis module  150  may analyze attributes of data in a dataset in order to discover potentially identifying information. Vulnerability analysis module  150  may automatically identify direct identifiers and quasi-identifiers in data, as well as sensitive attributes, such as a person&#39;s salary, medical history, and the like. Vulnerability analysis module  150  may seek and identify vulnerabilities on the basis of particular privacy regulations, guidelines, or other standards to which the de-identified data is expected to adhere. In some embodiments, vulnerability analysis module  150  finds identifiers by determining how data might be vulnerable to particular re-identification attacks, sensitive information disclosure attacks, membership disclosure attacks, inference attacks, or other exploits. A data owner may also indicate direct identifiers, quasi-identifiers, and/or sensitive attributes in a dataset. 
     Provider evaluation module  160  may associate each attribute identified by vulnerability analysis module  150  with one or more de-identification providers. Each de-identification provider may be a particular process for de-identifying or anonymizing data. For example, a de-identification provider may be a data masking algorithm for protecting email or postal addresses of individuals, or a data generalization method for abstracting data values related to individuals such as dates, diagnosis codes, purchased items, etc. Each de-identification provider may further be evaluated according to its configuration options. For example, a de-identification provider for dates may transform a specific date to a month and year, to a year, or to an interval of years. Each of these configuration options of the same de-identification provider may be evaluated by provider evaluation module  160  to determine a separate value for disclosure risk and data utility for each configuration option. For example, generalizing a date of birth value to a year value can significantly reduce privacy risk, but may also have a high impact on data utility, as several medical studies cannot be performed when date of birth is reduced to a year of birth granularity. 
     Once provider evaluation module  160  associates the identified attributes with one or more de-identification providers, provider evaluation module  160  may evaluate the associated providers with respect to the disclosure risk and data utility. Of the selected de-identification providers, one or more will be chosen, and applied to the dataset by de-identification module  170 . In some embodiments, provider evaluation module  160  automatically chooses a de-identification provider to apply to the dataset; for example, provider evaluation module  160  may select a de-identification provider (and respective configuration options) that is the most beneficial in terms of privacy and utility. In other embodiments, provider evaluation module  160  displays an evaluation of recommended de-identification providers to a user, such as a data owner, who may then select a de-identification provider to apply to the dataset. 
     De-identification module  170  applies de-identification providers to datasets in order to de-identify data. In particular, de-identification module  170  uses de-identification providers to protect direct and quasi-identifiers in datasets, so that they can no longer be used for performing re-identification attacks. Additionally, de-identification module  170  may protect sensitive attributes in a dataset, such as medical history or salary, from being accurately mapped to the corresponding individuals. De-identification module  170  may apply one or more de-identification providers that are selected by provider evaluation module  160  or by a user. De-identification module  170  may output de-identified data to storage  180  or database  120 . 
     Storage  180  may include any non-volatile storage media known in the art. For example, storage  180  can be implemented with a tape library, optical library, one or more independent hard disk drives, or multiple hard disk drives in a redundant array of independent disks (RAID). Similarly, data stored in storage  180  may conform to any suitable storage architecture known in the art, such as a file, a relational database, an object-oriented database, and/or one or more tables. A data owner may upload a dataset to storage  180  for de-identification. Storage  180  may store algorithms used by vulnerability analysis module  150  for identifying direct identifiers, quasi-identifiers, and sensitive attributes. Storage  180  may store de-identification providers for use by provider evaluation module  160  and de-identification module  170 . In some embodiments, de-identified data is output to storage  180 . 
       FIG. 2  is a flow chart depicting a method  200  of de-identifying data in accordance with an embodiment of the present invention. 
     Dataset attributes are selected for a vulnerability analysis at operation  210 . Attributes of a dataset may include any data and features thereof that could potentially contain personally identifiable information. Vulnerability analysis module  150  may scan a dataset&#39;s attributes for direct identifiers, quasi-identifiers, and sensitive attributes. Vulnerability analysis module  150  may discover quasi-identifiers by searching for combinations of attributes that together describe a single individual. In some embodiments, a user, such as a data owner, indicates the attributes that are direct identifiers, or quasi-identifiers, or sensitive attributes. 
     Each data attribute is associated with one or more de-identification providers at operation  220 . Provider evaluation module  160  may select de-identification providers that are appropriate for de-identifying the data attributes selected in operation  210 . Multiple de-identification providers may be associated with an attribute, as there can be more than one approach to de-identifying a particular data attribute. For example, a de-identification provider that generalizes dates by converting a date to a month and year, and de-identification provider that generalizes dates by converting a date to a year only, may both be associated with an attribute that is a date. A de-identification provider may also have several configuration options associated with an attribute. For example, if the attribute is a name, a de-identification provider may have configuration options to process the name by simply replacing the name with a randomly-selected name, replacing the name with another name associated with the same gender, processing name with a one-way hash provider (such as SHA-512) to generate a unique alphanumeric value for the name, etc. 
     For direct identifiers, the de-identification providers applied may include data masking techniques, data pseudonymization techniques, data encryption and hashing techniques. Data masking providers may obfuscate data by replacing the original data values with fictionalized values such that the dataset remains structurally similar, but is inauthentic. For quasi-identifiers, the de-identification providers may perform an anonymization technique that is based on data generalization and/or suppression operations, such as applying an algorithm that offers privacy guarantees following a formal privacy model like k-anonymity or l-diversity, among many others. Data generalization refers to the process of abstracting original data values in order to decrease their specificity. For example, a birth date may be generalized to a month and year, and further generalized to simply a year. Similarly, a telephone number may be a ten-digit number (e.g., 123-456-7890), which may be generalized to an area code and telephone prefix (e.g. 123-456-XXXX), and further generalized to the area code only (e.g., area code 123). Data suppression refers to the process of deleting data values. In some embodiments, data suppression is performed only when data generalization cannot adequately de-identify a dataset. 
     Each de-identification provider, including its supported configuration options, is evaluated with respect to its effects on data privacy and data utility at operation  230 . In general, a degree of de-identification generally has an inverse relationship to data utility; for example, a completely redacted document has no risk of containing identifying information, but such a document has no utility. Provider evaluation module  160  may evaluate all available de-identification providers (and configuration options thereof), the de-identification providers selected at operation  220 , or a subset of those selected de-identification providers. A data owner may use user device  110  to view the evaluations of provider evaluation module  160 . 
     De-identification providers may be evaluated according to several metrics. In order to evaluate the effect that de-identifying providers may have on data privacy, providers may be scored according to probabilistic data linkage and uniqueness criteria. Probabilistic data linkage may be used to evaluate providers by finding records that refer to the same entity across data sources. Probabilistic data linkage may be performed by provider evaluation module  160 . During probabilistic data linkage, a dataset may be compared against a publicly available dataset (e.g., census information, telephone directories, etc.) as well as a data owner&#39;s other datasets. To evaluate a de-identification provider based on uniqueness criteria, provider evaluation module  160  may determine how unique each record would remain after the application of a de-identification provider, how easily the data could be re-identified, and what background knowledge might be required to fill in gaps. Each de-identification provider that is evaluated may receive a score corresponding to its effect on data privacy. 
     De-identification providers may also be evaluated according to the effect that the providers have on data utility. Provider evaluation module  160  may rank providers based on a general information loss metric and a workload-aware metric. A general information loss metric may be calculated for each provider by quantifying the distortion introduced to the dataset by the provider. A workload-aware metric may be determined by evaluating the effect that a de-identification provider may have on the ability of the resulting dataset to correctly support an analytic workload, when compared to the ability of the original dataset to support the same analytic workload. For example, average relative error (ARE) is a measure of data utility that averages relative error of various aggregate queries applied on data. Relative error may be measured according to how far a particular answer of a COUNT query is in anonymized data compared to the same query applied on the original data; the closer the two answers are, the higher the utility of a de-identified dataset. 
     One or more de-identification providers are recommended at operation  240 . In some embodiments, the best de-identification providers are recommended based on their data privacy scores, data utility scores, or a combination of both. A privacy score may be computed according to the size of the smallest equivalence class, the change to the size of the smallest equivalence class before and after application of a provider, the distribution of the sizes of the equivalence classes, the number of quasi-identifying attributes and the possibility that these attributes are found in public datasets, population uniqueness criteria, etc. A utility score may be computed by comparing the performance of the de-identified dataset to a workload versus that of the original data, or by comparing the number of use-cases supported by the dataset before and after application of a provider. The recommendation may be presented to the data owner, who may then select a de-identification provider to apply to the dataset. Furthermore, each recommended anonymization technique may have several options that are presented to the data owner along with the effects of selecting those options. 
     The recommendations may be presented on a dashboard that enables a data owner to understand the effect that each different de-identification provider would have on their dataset. A data owner may also be warned that applying a particular de-identification provider may impact a dataset&#39;s utility to such an extent that the dataset will no longer support certain analytic tasks or use-cases. In some embodiments, any de-identification providers that would prevent a data owner from performing a particular analytic task or use-case on the dataset are not included in the recommendation. In some embodiments, any de-identification providers that block some analytic tasks or use-cases are highlighted so that the user is aware to avoid selecting them. 
     A selection of the de-identification providers is received at operation  250 . In some embodiments, a user, such as the data owner, may select a de-identification provider recommended during operation  240 . Provider evaluation module  160  may automatically select a de-identification provider based on the provider&#39;s data privacy score and data utility score relative to the other providers that were recommended at operation  240 . A machine learning engine may be employed in order to automatically select a de-identification provider. The machine learning engine may be trained to select a provider based on a combination of the data privacy score and the data utility score. The machine learning engine may employ various models to perform the learning (e.g., neural networks, mathematical/statistical models, classifiers, etc.). 
     The selected de-identification provider is used to apply changes to a dataset at operation  260 . In some embodiments, de-identification module  170  uses the de-identification provider selected by provider evaluation module  160  or a data owner during operation  250  to apply changes to the dataset. 
     Operation  270  determines whether the dataset is sufficiently de-identified. In some embodiments, a dataset&#39;s privacy metric is compared to a pre-defined threshold to determine whether the dataset is sufficiently de-identified. The threshold may be supplied by a data owner or may be required for the dataset to comply with a particular privacy regulation or standard. In one embodiment, a dataset&#39;s privacy metric is a number that corresponds to the size of the smallest equivalence class (e.g., the number of records having the same values for the quasi-identifiers) that was produced during de-identification. In some embodiments, the de-identification requirements of a legal framework, such as the Safe Harbor of the Health Insurance Portability and Accountability Act (HIPAA), or General Data Protection Regulation (GDPR) pseudonymization, are checked against the applied de-identification operations to the data to determine if the data are sufficiently protected under the corresponding legal framework. 
     If it is determined that the dataset has been sufficiently de-identified, then de-identification method  200  proceeds to operation  280  to output the de-identified dataset. If the dataset has not yet been sufficiently de-identified (e.g., its privacy metric does not meet the required threshold), then method  200  iterates by returning to operation  230 , and the de-identification provider and corresponding configuration options that were used are removed from consideration by provider evaluation module  160 . Since a previously-used de-identification provider is not included in additional repetitions of operations  230  through  260 , the overall de-identification process is made more efficient by avoiding the redundant re-application of providers to a dataset, thus using fewer computing resources such as processing time and memory. De-identification method  200  may iterate multiple times until the dataset is sufficiently de-identified, which may be determined at operation  270 . 
     The de-identified dataset is output at operation  280 . The de-identified dataset may be stored in storage  180 , database  120 , or sent to user device  110 . A user such as the data owner may interact with de-identified data on database  120  or storage  180  using user device  110 . 
       FIGS. 3A and 3B  illustrate examples of a dataset for de-identification in accordance with an embodiment of the present invention. As depicted,  FIGS. 3A and 3B  include dataset  300  with direct identifiers  310  and quasi-identifiers  320 . The fields and dimensions of dataset  300  are provided by way of example and are not intended to be limiting; datasets may be larger or smaller than dataset  300 . 
       FIG. 3A  depicts a dataset having direct identifiers  310  and quasi-identifiers  320 . Direct identifiers  310  include the fields “NAME,” “PHONE,” AND “SSN.” Quasi-identifiers  320  include the fields “AGE,” “GENDER,” and “ZIP CODE.”  FIG. 3B  depicts dataset  300  as various de-identification providers are selected for the attributes of dataset  300 . For example, de-identification provider DP 2  may be initially selected for de-identifying direct identifiers in the “NAME” field; likewise, de-identification providers DP 3  and DP 4  may be selected for the “PHONE” field and de-identification providers DP 1  with option  1  (op 1 ) for the “SSN” field. De-identification provider QP 1 , with options op 1  and op 2 , may be initially selected for de-identifying quasi-identifiers in the “AGE” and “GENDER” fields, and QP 2 , with options op 1 , op 2 , and op 3 , may be selected for de-identifying quasi-identifiers in the “AGE,” “GENDER,” and “ZIP CODE” fields. For example, these options may include an option for degree of de-identification (e.g., stricter de-identification at the expense of some data utility), an option for anonymizing according to the k-anonymity or the l-diversity privacy model, an option to control the degree of value suppression or extent of value generalization, an option to use domain generalization hierarchies in the anonymization process, etc. 
       FIG. 4A  illustrates an example of de-identification algorithm evaluation table  400  in accordance with an embodiment of the present invention. Evaluation table  400  may present an evaluation of the various de-identification providers selected in the example depicted in  FIGS. 3A and 3B . 
     As depicted, evaluation table  400  includes a privacy algorithm field  410 , privacy metrics field  420 , and utility metrics field  430 . Privacy algorithm field  410  may list various processes that are associated with attributes of the dataset and their respective configuration options. In some embodiments, the privacy processes correspond to de-identification providers selected by provider evaluation module  160  during operation  220  of method  200 . Privacy metrics field  320  may include subsections that correspond to specific measures of the effects that each algorithm has on a dataset&#39;s privacy, such as probabilistic data linkage and uniqueness criteria. Utility metrics field  430  may include subsections that correspond to specific measures of the effects that each process has on the dataset&#39;s utility. Each privacy metric and utility metric may be represented as a score that correlates to the selected algorithm&#39;s effect on data privacy or utility. 
     In some embodiments, an evaluation of the de-identification providers, such as evaluation  400 , is presented to a data owner, who may then select a provider based on the presented metrics. In some embodiments, the provider whose de-identification process offers the best benefits to privacy at the least expense to utility is selected automatically. For example, provider evaluation module  160  may select DP 2 , as it has higher privacy metric and utility metric scores compared to the other providers&#39; algorithms in evaluation table  400 , meaning that DP 2  minimizes privacy vulnerabilities in the dataset with very little impact to data utility. Selection of de-identification provider DP 2  may correspond to operation  250  of de-identification method  200 . A machine learning engine may be employed in order to automatically select a de-identification provider. The machine learning engine may be trained to select a provider based on a combination of the data privacy score and the data utility score. The machine learning engine may employ various models to perform the learning (e.g., neural networks, mathematical/statistical models, classifiers, etc.). 
     In some embodiments, evaluation  400  is presented to a data owner in the form of a dashboard. A dashboard may include visualizations of the effects that each de-identification provider would have when applied to the data owner&#39;s dataset. By presenting privacy and utility metrics in a dashboard format, a data owner may easily understand effects of different algorithms in order to select the desired algorithm and configuration options. 
       FIG. 4B  illustrates an example of a single de-identification provider evaluation dashboard  402  in accordance with an embodiment of the present invention. Single de-identification provider evaluation dashboard  402  presents an evaluation to a user of a single de-identification provider&#39;s effect on a particular attribute (or merged combination of attributes), as calculated by various metrics. Single de-identification provider evaluation dashboard  402  includes risk metric panes  440 , each of which displays a particular risk metric measuring the effect of applying a de-identification provider to an attribute by displaying a data privacy score  442  and privacy risk dial indicator  444 . Single de-identification provider evaluation dashboard  402  also includes utility loss metric panes  450 , each of which displays a particular utility loss metric measuring the effects of applying a de-identification provider to an attribute by displaying a utility loss score  452  and utility loss dial indicator  454 . A user, such as a data owner, may view and interact with single de-identification provider evaluation dashboard  402  using a device such as user device  110 . Provider evaluation module  160  may generate and update single de-identification provider evaluation dashboard  402 . 
     Data privacy score  442  may indicate a numerical value associated with the application of a particular de-identification provider in terms of its effect on a dataset&#39;s privacy risk, and may be calculated according to such metrics as probabilistic data linkage and/or uniqueness criteria. As depicted, a higher data privacy score  442  (e.g. closer to 1.0) may be associated with a higher risk, and a lower score (e.g., closer to zero) may be associated with a lower risk. Each privacy risk dial indicator  444  may display a visual representation of each data privacy score  442 . 
     Utility loss score  452  may indicate a numerical value associated with the application of a particular de-identification provider in terms of its effect on a dataset&#39;s utility, and may be calculated according to such metrics as a general information loss metric and a workload-aware metric. As depicted, a higher utility loss score  452  (e.g., closer to 1.0) may correspond to a greater loss of data utility, and a lower score (e.g., closer to zero) may correspond to a lesser loss of data utility. Each utility loss dial indicator  454  may display a visual representation of each utility loss score  452 . 
       FIG. 4C  illustrates an example of a multiple de-identification provider evaluation dashboard  404  in accordance with an embodiment of the present invention. Multiple de-identification provider evaluation dashboard  404  presents an evaluation to a user of various de-identification providers as applied to many different attributes in one display. As depicted, multiple de-identification provider evaluation dashboard  404  includes attribute panes  460  and corresponding bar charts  462 . A user, such as a data owner, may view and interact with multiple de-identification provider evaluation dashboard  404  using a device such as user device  110 . Provider evaluation module  160  may generate and update multiple de-identification provider evaluation dashboard  404 . 
     Each attribute pane  460  may show the effect of a particular de-identification provider as applied to a particular attribute. For example, “cmr_name:NAME:gender” may represent a provider that de-identifies the name while preserving gender information (so that male names are replaced with other male names, and female names are replaced with other female names from a list), whereas “cmr_name:NAME:random” may represent a provider that replaces a name with a randomly-selected name without guaranteeing gender preservation. Each bar chart  462  may illustrate the effects of applying the de-identification provider to an attribute, as measured by a particular metric, to generate one or more data privacy scores and/or utility loss scores. Multiple de-identification provider evaluation module  404  may also indicate which provider is optimal to apply to a particular attribute by highlighting one or more attribute panes  460 . 
       FIG. 5  is a block diagram depicting components of a computer  10  suitable for executing the methods disclosed herein. Computer  10  may enable user device  110 , database  120 , and server  140  to perform data de-identification in accordance with embodiments of the present invention. It should be appreciated that  FIG. 5  provides only an illustration of one embodiment and does not imply any limitations with regard to the environments in which different embodiments may be implemented. Many modifications to the depicted environment may be made. 
     As depicted, the computer  10  includes communications fabric  12 , which provides communications between computer processor(s)  14 , memory  16 , persistent storage  18 , communications unit  20 , and input/output (I/O) interface(s)  22 . Communications fabric  12  can be implemented with any architecture designed for passing data and/or control information between processors (such as microprocessors, communications and network processors, etc.), system memory, peripheral devices, and any other hardware components within a system. For example, communications fabric  12  can be implemented with one or more buses. 
     Memory  16  and persistent storage  18  are computer readable storage media. In the depicted embodiment, memory  16  includes random access memory (RAM)  24  and cache memory  26 . In general, memory  16  can include any suitable volatile or non-volatile computer readable storage media. 
     One or more programs may be stored in persistent storage  18  for execution by one or more of the respective computer processors  14  via one or more memories of memory  16 . The persistent storage  18  may be a magnetic hard disk drive, a solid state hard drive, a semiconductor storage device, read-only memory (ROM), erasable programmable read-only memory (EPROM), flash memory, or any other computer readable storage media that is capable of storing program instructions or digital information. 
     The media used by persistent storage  18  may also be removable. For example, a removable hard drive may be used for persistent storage  18 . Other examples include optical and magnetic disks, thumb drives, and smart cards that are inserted into a drive for transfer onto another computer readable storage medium that is also part of persistent storage  18 . 
     Communications unit  20 , in these examples, provides for communications with other data processing systems or devices. In these examples, communications unit  20  includes one or more network interface cards. Communications unit  20  may provide communications through the use of either or both physical and wireless communications links. 
     I/O interface(s)  22  allows for input and output of data with other devices that may be connected to computer  10 . For example, I/O interface  22  may provide a connection to external devices  28  such as a keyboard, keypad, a touch screen, and/or some other suitable input device. External devices  28  can also include portable computer readable storage media such as, for example, thumb drives, portable optical or magnetic disks, and memory cards. 
     Software and data used to practice embodiments of the present invention can be stored on such portable computer readable storage media and can be loaded onto persistent storage  18  via I/O interface(s)  22 . I/O interface(s)  22  may also connect to a display  30 . Display  30  provides a mechanism to display data to a user and may be, for example, a computer monitor. 
     The programs described herein are identified based upon the application for which they are implemented in a specific embodiment of the invention. However, it should be appreciated that any particular program nomenclature herein is used merely for convenience, and thus the invention should not be limited to use solely in any specific application identified and/or implied by such nomenclature. 
     Data in any dataset, whether de-identified not, may be stored within any conventional or other data structures (e.g., files, arrays, lists, stacks, queues, records, etc.) and may be stored in any desired storage unit (e.g., database, data or other repositories, queue, etc.) The data transmitted between user device  110 , database  120 , and server  140  may include any desired format and arrangement, and may include any quantity of any types of fields of any size to store the data. The definition and data model for any datasets may indicate the overall structure in any desired fashion (e.g., computer-related languages, graphical representation, listing, etc.). 
     Data in a dataset may include any information provided by user device  110 , database  120 , and storage  180 . Data in a dataset may include any desired format and arrangement, and may include any quantity of any types of fields of any size to store any desired data. The fields may indicate the presence, absence, actual values, or any other desired characteristics of the data of interest (e.g., quantity, value ranges, etc.). Data in a dataset may include all or any desired portion (e.g., any quantity of specific fields) of personal information (PI) or other data of interest within a given implementation or system. Data in a dataset may indicate the overall structure in any desired fashion (e.g., computer-related languages, graphical representation, listing, etc.). The fields for the dataset may be selected automatically (e.g., based on metadata, common or pre-defined models or structures, etc.) or manually (e.g., pre-defined, supplied by a data owner, etc.) in any desired fashion for a particular implementation or system. Metadata (e.g., for field selection, dataset properties etc.) may include any suitable information providing a description of fields or information (e.g., description of content, data type, etc.). 
     The data in a dataset may include any data collected about entities by any collection mechanism, any combination of collected information, any information derived from analyzing collected information, and any combination data before or after de-identification. 
     The present invention embodiments may employ any number of any type of user interface (e.g., Graphical User Interface (GUI), command-line, prompt, etc.) for obtaining or providing information (e.g., data in a dataset), where the interface may include any information arranged in any fashion. The interface may include any number of any types of input or actuation mechanisms (e.g., buttons, icons, fields, boxes, links, etc.) disposed at any locations to enter/display information and initiate desired actions via any suitable input devices (e.g., mouse, keyboard, etc.). The interface screens may include any suitable actuators (e.g., links, tabs, etc.) to navigate between the screens in any fashion. 
     The present invention embodiments are not limited to the specific tasks or algorithms described above, but may be utilized for generation and analysis of various types of data, even in the absence of that data. For example, present invention embodiments may be utilized for any types of data interest (e.g., sensitive data (personal information (PI) including information pertaining to patients, customers, suppliers, citizens, and/or employees, etc.) non-sensitive data, data that may become unavailable (e.g., data that is subject to deletion after retention for a minimum time interval (e.g., information subject to various regulations, etc.), information that becomes unavailable due to system outage, power failure, or other data loss, etc.), etc.). Further, present invention embodiments may generate and utilize any quantity of data regarding entities of interest. 
     It will be appreciated that the embodiments described above and illustrated in the drawings represent only a few of the many ways of implementing embodiments for iterative execution of data de-identification processes. 
     The environment of the present invention embodiments may include any number of computer or other processing systems (e.g., client or end-user systems, server systems, etc.) and databases or other repositories arranged in any desired fashion, where the present invention embodiments may be applied to any desired type of computing environment (e.g., cloud computing, client-server, network computing, mainframe, stand-alone systems, etc.). The computer or other processing systems employed by the present invention embodiments may be implemented by any number of any personal or other type of computer or processing system (e.g., desktop, laptop, PDA, mobile devices, etc.), and may include any commercially available operating system and any combination of commercially available and custom software (e.g., browser software, communications software, server software, vulnerability analysis module  150 , provider evaluation module  160 , de-identification module  170 , etc.). These systems may include any types of monitors and input devices (e.g., keyboard, mouse, voice recognition, etc.) to enter and/or view information. 
     It is to be understood that the software (e.g., client software, server software, communication software, vulnerability analysis module  150 , provider evaluation module  160 , de-identification module  170 , etc.) of the present invention embodiments may be implemented in any desired computer language and could be developed by one of ordinary skill in the computer arts based on the functional descriptions contained in the specification and flow charts illustrated in the drawings. Further, any references herein of software performing various functions generally refer to computer systems or processors performing those functions under software control. The computer systems of the present invention embodiments may alternatively be implemented by any type of hardware and/or other processing circuitry. 
     The various functions of the computer or other processing systems may be distributed in any manner among any number of software and/or hardware modules or units, processing or computer systems and/or circuitry, where the computer or processing systems may be disposed locally or remotely of each other and communicate via any suitable communications medium (e.g., LAN, WAN, Intranet, Internet, hardwire, modem connection, wireless, etc.). For example, the functions of the present invention embodiments may be distributed in any manner among the various end-user/client and server systems, and/or any other intermediary processing devices. The software and/or algorithms described above and illustrated in the flow charts may be modified in any manner that accomplishes the functions described herein. In addition, the functions in the flow charts or description may be performed in any order that accomplishes a desired operation. 
     The software of the present invention embodiments (e.g., vulnerability analysis module  150 , provider evaluation module  160 , de-identification module  170 , etc.) may be available on a non-transitory computer useable medium (e.g., magnetic or optical mediums, magneto-optic mediums, floppy diskettes, CD-ROM, DVD, memory devices, etc.) of a stationary or portable program product apparatus or device for use with stand-alone systems or systems connected by a network or other communications medium. 
     The communication network may be implemented by any number of any type of communications network (e.g., LAN, WAN, Internet, Intranet, VPN, etc.). The computer or other processing systems of the present invention embodiments may include any conventional or other communications devices to communicate over the network via any conventional or other protocols. The computer or other processing systems may utilize any type of connection (e.g., wired, wireless, etc.) for access to the network. Local communication media may be implemented by any suitable communication media (e.g., local area network (LAN), hardwire, wireless link, Intranet, etc.). 
     The system may employ any number of any conventional or other databases, data stores or storage structures (e.g., files, databases, data structures, data or other repositories, etc.) to store information (e.g., data in a dataset). The database system may be implemented by any number of any conventional or other databases, data stores or storage structures (e.g., files, databases, data structures, data or other repositories, etc.) to store information (e.g., data in a dataset). The database system may be included within or coupled to the server and/or client systems. The database systems and/or storage structures may be remote from or local to the computer or other processing systems, and may store any desired data (e.g., data in a dataset). 
     The present invention embodiments may employ any number of any type of user interface (e.g., Graphical User Interface (GUI), command-line, prompt, etc.) for obtaining or providing information (e.g., data in a dataset), where the interface may include any information arranged in any fashion. The interface may include any number of any types of input or actuation mechanisms (e.g., buttons, icons, fields, boxes, links, etc.) disposed at any locations to enter/display information and initiate desired actions via any suitable input devices (e.g., mouse, keyboard, etc.). The interface screens may include any suitable actuators (e.g., links, tabs, etc.) to navigate between the screens in any fashion. 
     The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used herein, the singular forms “a”, “an” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms “comprises”, “comprising”, “includes”, “including”, “has”, “have”, “having”, “with” and the like, when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof. 
     The corresponding structures, materials, acts, and equivalents of all means or step plus function elements in the claims below are intended to include any structure, material, or act for performing the function in combination with other claimed elements as specifically claimed. The description of the present invention has been presented for purposes of illustration and description, but is not intended to be exhaustive or limited to the invention in the form disclosed. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the invention. The embodiment was chosen and described in order to best explain the principles of the invention and the practical application, and to enable others of ordinary skill in the art to understand the invention for various embodiments with various modifications as are suited to the particular use contemplated. 
     The descriptions of the various embodiments of the present invention have been presented for purposes of illustration, but are not intended to be exhaustive or limited to the embodiments disclosed. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The terminology used herein was chosen to best explain the principles of the embodiments, the practical application or technical improvement over technologies found in the marketplace, or to enable others of ordinary skill in the art to understand the embodiments disclosed herein. 
     The present invention may be a system, a method, and/or a computer program product at any possible technical detail level of integration. The computer program product may include a computer readable storage medium (or media) having computer readable program instructions thereon for causing a processor to carry out aspects of the present invention. 
     The computer readable storage medium can be a tangible device that can retain and store instructions for use by an instruction execution device. The computer readable storage medium may be, for example, but is not limited to, an electronic storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. A non-exhaustive list of more specific examples of the computer readable storage medium includes the following: a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), a static random access memory (SRAM), a portable compact disc read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanically encoded device such as punch-cards or raised structures in a groove having instructions recorded thereon, and any suitable combination of the foregoing. A computer readable storage medium, as used herein, is not to be construed as being transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide or other transmission media (e.g., light pulses passing through a fiber-optic cable), or electrical signals transmitted through a wire. 
     Computer readable program instructions described herein can be downloaded to respective computing/processing devices from a computer readable storage medium or to an external computer or external storage device via a network, for example, the Internet, a local area network, a wide area network and/or a wireless network. The network may comprise copper transmission cables, optical transmission fibers, wireless transmission, routers, firewalls, switches, gateway computers and/or edge servers. A network adapter card or network interface in each computing/processing device receives computer readable program instructions from the network and forwards the computer readable program instructions for storage in a computer readable storage medium within the respective computing/processing device. 
     Computer readable program instructions for carrying out operations of the present invention may be assembler instructions, instruction-set-architecture (ISA) instructions, machine instructions, machine dependent instructions, microcode, firmware instructions, state-setting data, configuration data for integrated circuitry, or either source code or object code written in any combination of one or more programming languages, including an object oriented programming language such as Smalltalk, C++, or the like, and procedural programming languages, such as the “C” programming language or similar programming languages. The computer readable program instructions may execute entirely on the user&#39;s computer, partly on the user&#39;s computer, as a stand-alone software package, partly on the user&#39;s computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer may be connected to the user&#39;s computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet Service Provider). In some embodiments, electronic circuitry including, for example, programmable logic circuitry, field-programmable gate arrays (FPGA), or programmable logic arrays (PLA) may execute the computer readable program instructions by utilizing state information of the computer readable program instructions to personalize the electronic circuitry, in order to perform aspects of the present invention. 
     Aspects of the present invention are described herein with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer readable program instructions. 
     These computer readable program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks. These computer readable program instructions may also be stored in a computer readable storage medium that can direct a computer, a programmable data processing apparatus, and/or other devices to function in a particular manner, such that the computer readable storage medium having instructions stored therein comprises an article of manufacture including instructions which implement aspects of the function/act specified in the flowchart and/or block diagram block or blocks. 
     The computer readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device to cause a series of operational steps to be performed on the computer, other programmable apparatus or other device to produce a computer implemented process, such that the instructions which execute on the computer, other programmable apparatus, or other device implement the functions/acts specified in the flowchart and/or block diagram block or blocks. 
     The flowchart and block diagrams in the Figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of instructions, which comprises one or more executable instructions for implementing the specified logical function(s). In some alternative implementations, the functions noted in the blocks may occur out of the order noted in the Figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems that perform the specified functions or acts or carry out combinations of special purpose hardware and computer instructions.