Srivatsava Ranjit Ganta
ABSTRACT
Privacy preservation is currently one of the key challenges in enterprise data management. Data Anonymization techniques address this by sanitizing and releasing anonymized data such that enterprises can share and disseminate sensitive information without compromising consumer privacy. However, current anonymization techniques are prone to attacks where-in an intruder can fuse external information with the anonymized data to infer sensitive information. In this paper, we pose and formulate the problem of Information Fusion based Privacy Attack. We experimentally demonstrate such an attack on a publicly available data set. We propose adaptive anonymization schemes to address this problem and experimentally demonstrate a prototype solution.
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Digital Library
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Index Terms
- Adaptive data anonymization against information fusion based privacy attacks on enterprise data
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