A Survey on Privacy Properties for Data Publishing of Relational Data

Abstract

Recent advances in telecommunications and database systems have allowed the scientific community to efficiently mine vast amounts of information worldwide and to extract new knowledge by discovering hidden patterns and correlations. Nevertheless, all this shared information can be used to invade the privacy of individuals through the use of fusion and mining techniques. Simply removing direct identifiers such as name, SSN, or phone number is not anymore sufficient to prevent against these practices. In numerous cases, other fields, like gender, date of birth and/or zipcode, can be used to re-identify individuals and to expose their sensitive details, e.g. their medical conditions, financial statuses and transactions, or even their private connections. The scope of this work is to provide an in-depth overview of the current state of the art in Privacy-Preserving Data Publishing (PPDP) for relational data. To counter information leakage, a number of data anonymisation methods have been proposed during the past few years, including k-anonymity,-diversity, t-closeness, to name a few. In this study we analyse these methods providing concrete examples not only to explain how each of them works, but also to facilitate the reader to understand the different usage scenarios in which each of them can be applied. Furthermore, we detail several attacks along with their possible countermeasures, and we discuss open questions and future research directions.

Improving Multivariate Microaggregation through Hamiltonian Paths and Optimal Univariate Microaggregation

“…Moreover, adjusting TSP heuristics to leverage lightweight microaggregationbased approaches is an interesting research path to follow. In addition, although the values of the privacy parameter k are typically low (i.e., 3,4,5,6), we plan to study the effect of larger values of k on our solution. Last but not least, since microaggregation is essentially a data-oriented procedure, we will study how our solution adapts to data structures from specific domains, such as healthcare, transportation, energy, and the like.…”

“…Other approaches have focused on the efficiency of the microaggregation procedure, for example, the Fast Data-oriented Microaggregation (FDM) method proposed by Mortazavi et al [25] efficiently anonymizes large multivariate numerical datasets for multiple successive values of k. The interested readers can find more detailed information about microaggregation in Reference [5,26].…”

“…Therefore, before disclosing any data, privacy protection procedures (e.g., anonymization, pseudonymization, aggregation, generalization) must be applied. A wide variety of privacy models and protection mechanisms have been proposed in the literature so as to guarantee anonymity (at different levels depending on the utilized model) when disclosing data [5]. Since most privacy protection methods are based on modifying/perturbing/deleting original data, their main drawback is that they negatively affect the utility of the data.…”

Anonymization Techniques for Privacy Preserving Data Publishing: A Comprehensive Survey

“…In this conceptual overview, we mainly present the overview of data collected from the individuals, different actors involved in the anonymization scenario, anonymization techniques applied on respective data, anonymous data to be published for analytics/mining purposes, and privacy breaches that can occur during published data analytics. The typical PPDP scenario involves five types of actors [57]. The brief description about each actor along with examples is summarized in Figure 4 (b).…”

Big Data-Driven AI-based Wireless Network Personalization

“…Primarily, user privacy is realized by data anonymization [124]. The main idea behind this method revolves around encrypting/removing any type of personally identifiable data.…”