A hybrid approach to prevent composition attacks for independent data releases

Li, Jiuyong; Baig, Muzammil M.; Sattar, Abdus; Ding, Xiuli; Liu, Jixue; Vincent, Millist W.

doi:10.1016/j.ins.2016.05.009

Cited by 20 publications

(35 citation statements)

References 13 publications

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“…In the last decade, Hybrid [2], Probabilistic [23] and Composition [24] privacy methods have been proposed for multiple independent data publishing. Composition is the first privacy model to prevent against composition attacks in the multiple independent data publishing context.…”

Section: Related Workmentioning

confidence: 99%

“…In − DP privacy, if the independent datasets privacy are preserved by the privacy budget , then the smaller value provides the higher privacy protection. It is observed in [2,39,40] that using − DP to protect from composition attacks generates a significant amount of loss of data utility during anonymization. Most of the differential privacy methods support interactive settings to satisfy the − DP requirements.…”

Section: Related Workmentioning

confidence: 99%

“…This is a reasonable assumption since public and private groups may have a standard method for data distribution. For example, all health care institutions in the USA follow one law, the HIPAA, for their distribution of d-identified data [2].…”

Section: The Quasi-identifier Of T and T[s]mentioning

confidence: 99%

“…However, the publishing of data may jeopardize personal privacy and disclose the sensitive values [1]. In recent decades, the sharing of personal data has resulted in numerous incidents involving data privacy breaches [2,3], with disastrous results for the reputations and finances of organizations. Privacy-preserving data publishing methods are anonymizing the published data to preserve user privacy while allowing organizations to release their datasets [4].…”

Section: Introductionmentioning

confidence: 99%

“…In addition, we published earlier research on privacy-preserving data publishing [15,17,22]. To reduce the likelihood of composition attacks on published datasets, existing anonymization techniques [2,23,24] utilize generalization and perturbation, which decrease the data utility. In this paper, we use some ideas from [17] and propose a new approach called Merging for protection against composition attack in various independent data publications while preserving better data utility.…”

Section: Introductionmentioning

confidence: 99%

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A New Approach to Privacy-Preserving Multiple Independent Data Publishing

et al. 2018

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Featured Application: The Merging method might apply to publish the datasets sequentially from the different organizations where it will ensure more data utility and privacy. Abstract:We study the problem of privacy preservation in multiple independent data publishing. An attack on personal privacy which uses independent datasets is called a composition attack. For example, a patient might have visited two hospitals for the same disease, and his information is independently anonymized and distributed by the two hospitals. Much of the published work makes use of techniques that reduce data utility as the price of preventing composition attacks on published datasets. In this paper, we propose an innovative approach to protecting published datasets from composition attack. Our cell generalization approach increases both protection of individual privacy from composition attack and data utility. Experimental results show that our approach can preserve more data utility than the existing methods.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: The Quasi-identifier Of T and T[s]mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%