- Title
- Quantifying privacy: a novel entropy-based measure of disclosure risk
- Creator
- Alfalayleh, Mousa; Brankovic, Ljiljana
- Relation
- 25th International Workshop on Combinatorial Algorithms (IWOCA 2014). Proceedings of the 25th International Workshop on Combinatorial Algorithms [presented in Lecture Notes in Computer Science, Vol. 8986] (Duluth, MN 15-17 October, 2014) p. 24-36
- Publisher Link
- http://dx.doi.org/10.1007/978-3-319-19315-1_3
- Publisher
- Springer
- Resource Type
- conference paper
- Date
- 2015
- Description
- It is well recognised that data mining and statistical analysis pose a serious treat to privacy. This is true for financial, medical, criminal and marketing research. Numerous techniques have been proposed to protect privacy, including restriction and data modification. Recently proposed privacy models such as differential privacy and kanonymity received a lot of attention and for the latter there are now several improvements of the original scheme, each removing some security shortcomings of the previous one. However, the challenge lies in evaluating and comparing privacy provided by various techniques. In this paper we propose a novel entropy based security measure that can be applied to any generalisation, restriction or data modification technique. We use our measure to empirically evaluate and compare a few popular methods, namely query restriction, sampling and noise addition.
- Subject
- privacy; data mining; statistical analysis; data modification; privacy models
- Identifier
- http://hdl.handle.net/1959.13/1340294
- Identifier
- uon:28438
- Identifier
- ISBN:9783319193144
- Language
- eng
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