Noiseless Database Privacy

  • Raghav Bhaskar
  • Abhishek Bhowmick
  • Vipul Goyal
  • Srivatsan Laxman
  • Abhradeep Thakurta
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7073)


Differential Privacy (DP) has emerged as a formal, flexible framework for privacy protection, with a guarantee that is agnostic to auxiliary information and that admits simple rules for composition. Benefits notwithstanding, a major drawback of DP is that it provides noisy responses to queries, making it unsuitable for many applications. We propose a new notion called Noiseless Privacy that provides exact answers to queries, without adding any noise whatsoever. While the form of our guarantee is similar to DP, where the privacy comes from is very different, based on statistical assumptions on the data and on restrictions to the auxiliary information available to the adversary. We present a first set of results for Noiseless Privacy of arbitrary Boolean-function queries and of linear Real-function queries, when data are drawn independently, from nearly-uniform and Gaussian distributions respectively. We also derive simple rules for composition under models of dynamically changing data.


Boolean Function Privacy Protection Auxiliary Information Constant Fraction Database Entry 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© International Association for Cryptologic Research 2011

Authors and Affiliations

  • Raghav Bhaskar
    • 1
  • Abhishek Bhowmick
    • 2
  • Vipul Goyal
    • 1
  • Srivatsan Laxman
    • 1
  • Abhradeep Thakurta
    • 3
  1. 1.Microsoft ResearchIndia
  2. 2.University of TexasAustinUSA
  3. 3.Pennsylvania State UniversityUSA

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