• About
  • Policies
  • What is open access
  • Library
  • Contact
Advanced search
      View Item 
      •   BUIR Home
      • Scholarly Publications
      • Faculty of Engineering
      • Department of Computer Engineering
      • View Item
      •   BUIR Home
      • Scholarly Publications
      • Faculty of Engineering
      • Department of Computer Engineering
      • View Item
      JavaScript is disabled for your browser. Some features of this site may not work without it.

      Differentially private binary- and matrix-valued data query: an XOR mechanism

      Thumbnail
      View / Download
      1.2 Mb
      Author(s)
      Ji, T.
      Li, P.
      Yilmaz, E.
      Ayday, Erman
      Ye, Y. F.
      Sun, J.
      Date
      2021-01
      Source Title
      Proceedings of the VLDB Endowment
      Electronic ISSN
      2150-8097
      Publisher
      Association for Computing Machinery
      Volume
      14
      Issue
      5
      Pages
      849 - 862
      Language
      English
      Type
      Article
      Item Usage Stats
      38
      views
      92
      downloads
      Abstract
      Differential privacy has been widely adopted to release continuous- and scalar-valued information on a database without compromising the privacy of individual data records in it. The problem of querying binary- and matrix-valued information on a database in a differentially private manner has rarely been studied. However, binary- and matrix-valued data are ubiquitous in real-world applications, whose privacy concerns may arise under a variety of circumstances. In this paper, we devise an exclusive or (XOR) mechanism that perturbs binary- and matrix-valued query result by conducting an XOR operation on the query result with calibrated noises attributed to a matrix-valued Bernoulli distribution. We first rigorously analyze the privacy and utility guarantee of the proposed XOR mechanism. Then, to generate the parameters in the matrix-valued Bernoulli distribution, we develop a heuristic approach to minimize the expected square query error rate under ϵ-differential privacy constraint. Additionally, to address the intractability of calculating the probability density function (PDF) of this distribution and efficiently generate samples from it, we adapt an Exact Hamiltonian Monte Carlo based sampling scheme. Finally, we experimentally demonstrate the efficacy of the XOR mechanism by considering binary data classification and social network analysis, all in a differentially private manner. Experiment results show that the XOR mechanism notably outperforms other state-of-the-art differentially private methods in terms of utility (such as classification accuracy and F1 score), and even achieves comparable utility to the non-private mechanisms.
      Permalink
      http://hdl.handle.net/11693/77562
      Published Version (Please cite this version)
      https://doi.org/10.14778/3446095.3446106
      Collections
      • Department of Computer Engineering 1561
      Show full item record

      Browse

      All of BUIRCommunities & CollectionsTitlesAuthorsAdvisorsBy Issue DateKeywordsTypeDepartmentsCoursesThis CollectionTitlesAuthorsAdvisorsBy Issue DateKeywordsTypeDepartmentsCourses

      My Account

      Login

      Statistics

      View Usage StatisticsView Google Analytics Statistics

      Bilkent University

      If you have trouble accessing this page and need to request an alternate format, contact the site administrator. Phone: (312) 290 2976
      © Bilkent University - Library IT

      Contact Us | Send Feedback | Off-Campus Access | Admin | Privacy