Anonymizing sequential releases under arbitrary updates

Abstract

In today's global information society, governments, companies, public and private institutions and even individuals have to cope with growing demands for personal data publication from scientists, statisticians, journalists and many other data consumers. Current researches on privacy-preserving data publishing by sanitization focus on static dataset, which have no updates. In real life however, data sources are dynamic and usually the updates in these datasets are mainly arbitrary. Then, applying any popular static privacy-preserving technique inevitably yields to information disclosure. Among the few works in the literature that relate to the serial data publication, none of them focuses on arbitrary updates, i.e. with any consistent insert/update/delete sequence, and especially in the presence of auxiliary knowledge that tracks updates of individuals. In this communication, we first highlight the invalidation of existing algorithms and present an extension of the m-invariance generalization model coined τ -safety. Then we formally state the problem of privacypreserving dataset publication of sequential releases in the presence of arbitrary updates and chainability-based background knowledge. We also propose an approximate algorithm, and we show that our approach to τ -safety, not only prevents from any privacy breach but also achieve a high utility of the anonymous releases.

Improved privacy preserving method for periodical SRS publishing

“…The tradeoff between privacy and utility is the focus in data publishing. The traditional periodical data publishing mechanisms [9][10][11][12][13][14] are not suitable for the SRS data due to its some special…”

“…Dynamic data publishing [10,[12][13][14]: periodic publishing where records can be added, deleted or updated from previously released tables. This method cannot preserve the identities of individuals among different tables.…”

“…K-anonymity [3] and its variants [4][5][6][7][8] are only suitable for static tables. When faced with Dynamic data tables, some incremental data publishing methods [9][10][11][12][13][14] are presented, such as BCF-anonymity [9], m-invariance [10], etc. Most techniques cannot preserve identity in released tables and are not well suited to SRS data publishing.…”

Privacy preservation in e-health cloud: taxonomy, privacy requirements, feasibility analysis, and opportunities

“…In the case of dynamic data publication, m-invariance [118] and τ-safety [134] use generalization to achieve the given privacy requirements. The generalization-based minvariance and τ-safety cause high information loss [128].…”

A Survey of Privacy Preserving Data Publishing using Generalization and Suppression

“…Generalization hierarchy is incorporated to implement user-oriented anonymization for public data in [64]. Adeel et al [65] also use generalization operation coping with the problem of sequential release under arbitrary update. Mahesh et al [66] propose a new method to preserve individuals sensitive data from record and attribute linkage attacks by setting range values and record elimination.…”