An Approach to Reducing Information Loss and Achieving Diversity of Sensitive Attributes in k-anonymity Methods

Abstract

Electronic Health Records (EHRs) enable the sharing of patients’ medical data. Since EHRs include patients’ private data, access by researchers is restricted. Therefore k-anonymity is necessary to keep patients’ private data safe without damaging useful medical information. However, k-anonymity cannot prevent sensitive attribute disclosure. An alternative, l-diversity, has been proposed as a solution to this problem and is defined as: each Q-block (ie, each set of rows corresponding to the same value for identifiers) contains at least l well-represented values for each sensitive attribute. While l-diversity protects against sensitive attribute disclosure, it is limited in that it focuses only on diversifying sensitive attributes. The aim of the study is to develop a k-anonymity method that not only minimizes information loss but also achieves diversity of the sensitive attribute. This paper proposes a new privacy protection method that uses conditional entropy and mutual information. This method considers both information loss as well as diversity of sensitive attributes. Conditional entropy can measure the information loss by generalization, and mutual information is used to achieve the diversity of sensitive attributes. This method can offer appropriate Q-blocks for generalization. We used the adult database from the UCI Machine Learning Repository and found that the proposed method can greatly reduce information loss compared with a recent l-diversity study. It can also achieve the diversity of sensitive attributes by counting the number of Q-blocks that have leaks of diversity. This study provides a privacy protection method that can improve data utility and protect against sensitive attribute disclosure. The method is viable and should be of interest for further privacy protection in EHR applications.

Privacy Policy and Technology in Biomedical Data Science

“…For example, Aristodimou et al (45) proposed a pattern-based multidimensional suppression technique (kPB-MS) for privacy-preserving data publishing to minimize the information loss through k -anonymity in which feature selection was incorporated to reduce data dimensionality and combine attributes for record suppression. Yoo et al (46) described a generalization method satisfying both k -anonymity and l -diversity that used conditional entropy to measure the loss of information as well as mutual information for sensitive attributes.…”

“Big Data” and the Electronic Health Record

“…In this approach, equally outdated, a quasi-identifier equivalence class, which contained rows of records sharing identical values of non-sensitive attributes, showed diverse values in each sensitive attribute. An updated k-anonymity method developed for EHR data adopts ideas from l-diversity to keep sensitive attributes diverse [113]. A new framework for protecting privacy has been more utilized lately.…”

Sharing privacy-sensitive access to neuroimaging and genetics data: a review and preliminary validation

“…The Health Insurance Portability and Accountability Act (HIPAA) privacy rule (45 CFR Part 160 and Subparts A and E of Part 164) allows the sharing of data as long as the data is de-identified. However, many approaches to anonymizing or “sanitizing” data sets (Sweeney, 2002 ; Li et al, 2007 ; Machanavajjhala et al, 2007 ; Xiao and Tao, 2007 ; Malin, 2008 ) are subject to attacks (Sweeney, 1997 ; Ganta et al, 2008 ; Narayanan and Shmatikov, 2008 ) that use public data to compromise privacy.…”

“…When data sharing itself is precluded, methods such as k -anonymity (Sweeney, 2002 ), l -diversity (Machanavajjhala et al, 2007 ), t -closeness (Li et al, 2007 ), and m -invariance (Xiao and Tao, 2007 ) are no longer appropriate, since they deal with constructing private or sanitized versions of the data itself. In such situations we would want to construct data access systems in which data holders do not share the data itself but instead provide an interface to the data that allows certain pre-specified computations to be performed on that data.…”