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Privbayes : private data release via Bayesian networks
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Zhang, Jun, Cormode, Graham, Procopiuc, Cecilia, Srivastava, Divesh and Xiao, Xiaokui (2014) Privbayes : private data release via Bayesian networks. In: ACM SIGMOD Conference, Salt Lake City, Utah, 22-27 Jun 2014. Published in: Proceedings of the 2014 ACM SIGMOD international conference on Management of data pp. 1423-1434. ISBN 9781450323765. doi:10.1145/2588555.2588573
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Official URL: http://dx.doi.org/10.1145/2588555.2588573
Abstract
Privacy-preserving data publishing is an important problem that has been the focus of extensive study. The state-of-the-art goal for this problem is differential privacy, which offers a strong degree of privacy protection without making restrictive assumptions about the adversary. Existing techniques using differential privacy, however, cannot effectively handle the publication of high-dimensional data. In particular, when the input dataset contains a large number of attributes, existing methods require injecting a prohibitive amount of noise compared to the signal in the data, which renders the published data next to useless. To address the deficiency of the existing methods, this paper presents PrivBayes, a differentially private method for releasing high-dimensional data. Given a dataset D, PrivBayes first constructs a Bayesian network N, which (i) provides a succinct model of the correlations among the attributes in D and (ii) allows us to approximate the distribution of data in D using a set P of low-dimensional marginals of D. After that, PrivBayes injects noise into each marginal in P to ensure differential privacy, and then uses the noisy marginals and the Bayesian network to construct an approximation of the data distribution in D. Finally, PrivBayes samples tuples from the approximate distribution to construct a synthetic dataset, and then releases the synthetic data. Intuitively, PrivBayes circumvents the curse of dimensionality, as it injects noise into the low-dimensional marginals in P instead of the high-dimensional dataset D. Private construction of Bayesian networks turns out to be significantly challenging, and we introduce a novel approach that uses a surrogate function for mutual information to build the model more accurately. We experimentally evaluate PrivBayes on real data, and demonstrate that it significantly outperforms existing solutions in terms of accuracy.
Item Type: | Conference Item (Paper) | ||||
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Subjects: | Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software | ||||
Divisions: | Faculty of Science, Engineering and Medicine > Science > Computer Science | ||||
Library of Congress Subject Headings (LCSH): | Database security, Confidential communications, Data protection, Bayesian statistical decision theory -- Data processing, Neural networks (Computer science), Electronic data processing, Data mining, Computer simulation | ||||
Journal or Publication Title: | Proceedings of the 2014 ACM SIGMOD international conference on Management of data | ||||
Publisher: | ACM | ||||
ISBN: | 9781450323765 | ||||
Official Date: | 22 June 2014 | ||||
Dates: |
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Page Range: | pp. 1423-1434 | ||||
DOI: | 10.1145/2588555.2588573 | ||||
Status: | Peer Reviewed | ||||
Publication Status: | Published | ||||
Access rights to Published version: | Restricted or Subscription Access | ||||
Date of first compliant deposit: | 28 December 2015 | ||||
Date of first compliant Open Access: | 28 December 2015 | ||||
Funder: | Singapore. Nanyang Technological University (NTU), Microsoft Research Asia, Singapore. Ministry of Education | ||||
Grant number: | M4080094.020 (NTU) ; AcRF Tier-2 (Ministry of Education) | ||||
Adapted As: | http://wrap.warwick.ac.uk/92273/ | ||||
Embodied As: | 1 | ||||
Conference Paper Type: | Paper | ||||
Title of Event: | ACM SIGMOD Conference | ||||
Type of Event: | Conference | ||||
Location of Event: | Salt Lake City, Utah | ||||
Date(s) of Event: | 22-27 Jun 2014 | ||||
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