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Volume 6 Issue 2

On Syntactic Anonymity and Differential Privacy

Chris Clifton^(a), Tamir Tassa^(b),(*)

Transactions on Data Privacy 6:2 (2013) 161 - 183

Abstract, PDF

(a) The Department of Mathematics and Computer Science; The Open University of Israel; 1 University Road; Ra'anana 4353701; Israel.

(b) Department of Computer Science/CERIAS; Purdue University; West Lafayette; IN 47907-2107 USA.

e-mail:clifton @cs.purdue.edu; tamirta @openu.ac.il

Abstract

Recently, there has been a growing debate over approaches for handling and analyzing private data. Research has identified issues with syntactic approaches such as k-anonymity and l-diversity. Differential privacy, which is based on adding noise to the analysis outcome, has been promoted as the answer to privacy-preserving data mining. This paper looks at the issues involved and criticisms of both approaches. We conclude that both approaches have their place, and that each approach has issues that call for further research. We identify these research challenges, and discuss recent developments and future directions that will enable greater access to data while improving privacy guarantees.

^* Corresponding author.

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