# Analytics on Anonymity for Privacy Retention in Smart Health Data +
###### Abstract
If an attacker is looking for [PERSON]'s home address and knows that [PERSON] is about 35, not only his home address can be found but his genetic disease and other personal information can also be revealed. Compared to [PERSON] being one of the age 30 female health club members, [PERSON] is more anonymous (among female club members) than [PERSON] (among those of age 30-35). Because unlike [PERSON], [PERSON] is not easily distinguishable since there are other people with the same information as [PERSON]. Since the attacker cannot identify [PERSON] among other age 30 female club members, she is more anonymous. Thus, [PERSON]'s privacy is better protected because she has higher anonymity than [PERSON]. Anonymity ensures that each set of 'critical' data values is associated with more than one individual (or a minimum requirement) to protect the individual's identity [8; 9; 10]. When the minimum requirement is specified to \(k\), we refer to such a condition as \(k\)-anonymity, where each unique 'critical' data values has at least \(k\) records (or people) [7; 8]. Much work on privacy and anonymity analytics has been studied in two groups: anonymity measures [11; 12; 13; 14] and anonymization techniques [8; 9; 10; 12; 14; 15; 16; 17; 18; 19; 20; 21; 22; 23; 24; 25]. The former deals with indirect anonymity measures based on relevant information (e.g., average information losses [13; 14], likelihood of attacker's correct re-identification given his prior knowledge [12]). The latter deals with transforming a given database into a more anonymous form to better protect privacy. Many anonymization techniques have been researched [8; 9; 10; 14; 15; 18; 19; 20; 21; 22] using suppression and generalization by either omitting [18; 20] or replacing critical data values with more general substitutes (according to its taxonomies) [22]. Some transforms the original data into a generalized table that complies with \(k\)-anonymity requirements [8; 9; 10; 19]. Many techniques have focused on minimizing generalizations to enhance computational efficiency. Still the challenge is not only to efficiently preserve data privacy but also to ensure that the shared data are sufficiently informative to be useful. Excess generalization to provide anonymity can corrupt important information that the data may convey. As a result, the data become less informative. Recent anonymization approaches [16; 17; 23] aim to increase data information (e.g., by using information gain [17] but they are intended to be used resulting anonymized data for classification. Our research studies health data analytics focusing on anonymity for privacy protection. This paper presents a multi-faceted analytical approach to (1) identifying attributes susceptible to information leakages by using entropy-based measure to analyze information loss, (2) anonymizing the data by generalization using attribute hierarchies, and (3) balancing between anonymity and informativeness by our anonymization technique that produces anonymized data satisfying a given anonymity requirement while optimizing data retention. Our anonymization technique is an automated Artificial Intelligence search based on two simple heuristics. Part (1) can be viewed as pre-anonymization analytics, whereas part (2) is anonymization with a refinement in part (3). Because there are many anonymization techniques and each may have different objectives, the post anonymization analytics can be performed by applying Part (1) again. The contribution of this paper is not only techniques for each part but an overall methodology for analyzing privacy and anonymity of health data. The proposed method is practical in a sense that it is applicable to any kind of smart health data and moreover balancing the information loss and anonymity makes the approach feasible to use. This paper is an extension from our previous work [15] that only describes part (3) focusing on our proposed anonymization algorithm. The rest of the paper is organized as follows.