A relative privacy model for effective privacy preservation in transactional data
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(Published version)
Date
2017
Authors
Bewong, M.
Liu, J.
Liu, L.
Li, J.
Choo, K.K.R.
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Conference paper
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Proceedings 16th IEEE International Conference on Trust Security and Privacy in Computing and Communications 11th IEEE International Conference on Big Data Science and Engineering and 14th IEEE International Conference on Embedded Software and Systems Trustcom Bigdatase Icess 2017, 2017, pp.394-401
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2017 IEEE Trustcom/BigDataSE/ICESS (1 Aug 2017 - 4 Aug 2017 : Sydney, Australia)
Abstract
Transactional data such as shopping logs, web search queries and medical notes present enormous opportunities for knowledge discovery through data mining. When such data is published for knowledge discovery, privacy disclosure risks arise, making privacy preserving publication a fundamental requirement. However, existing publication mechanisms do not fully prevent an adversary from making an inference about the intended victim. While some solutions to this problem exist for the publication of relational data, they are not transferable to the publication of transactional data due to the difference in data models. This work aims to prevent inference attacks in the publication of transactional data by proposing a relative privacy metric that ensures that the knowledge gain of an adversary about any individual from the published data is bound to the general public knowledge. We then propose a publication mechanism Anony, which satisfies the proposed privacy metric without having to use excessively large cluster sizes. Finally, we evaluate our publication mechanism using two benchmark datasets and the results demonstrate that the proposed mechanism is effective and efficient.
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Copyright 2017 IEEE
Access Condition Notes: post print available on open access