Certificate Verification        Student Ambassador          Quick Pay        Request For Enquiry
Sell Your Project      Apply for franchise          
  • 0877-2261612       
  • +91-9030 333 433
  • +91-9966 062 884

Multi-party High-dimensional Data Publishing Under Differential Privacy

MULTI-PARTY HIGH-DIMENSIONAL DATA PUBLISHING UNDER DIFFERENTIAL PRIVACY

  • Project Code :
  • TCREAN19_16
  • .
Buy Now

Cost will be available soon

Download Project Document / Synopsis

MULTI-PARTY HIGH-DIMENSIONAL DATA PUBLISHING UNDER DIFFERENTIAL PRIVACY

Multi-Party High-Dimensional Data Publishing under Differential Privacy

Abstract:

We study the problem of publishing high-dimensional data in a distributed multi-party environment under differential privacy. In particular, with the assistance of a semi-trusted curator, the parties (i.e., local data owners) collectively generate a synthetic integrated dataset while satisfying ε-differential privacy. To solve this problem, we present a differentially private sequential update of Bayesian network (DP-SUBN) approach. In DP-SUBN, the parties and the curator collaboratively identify the Bayesian network N that best fits the integrated dataset in a sequential manner, from which a synthetic dataset can then be generated. The fundamental advantage of adopting the sequential update manner is that the parties can treat the intermediate results provided by previous parties as their prior knowledge to direct how to learn N. The core of DP-SUBN is the construction of the search frontier, which can be seen as a priori knowledge to guide the parties to update N. By exploiting the correlations of attribute pairs, we propose exact and heuristic methods to construct the search frontier. In particular, to privately quantify the correlations of attribute pairs without introducing too much noise, we first put forward a non-overlapping covering design (NOCD) method, and then devise a dynamic programming method for determining the optimal parameters used in NOCD. Through privacy analysis, we show that DP-SUBN satisfies ε-differential privacy. Extensive experiments on real datasets demonstrate that DP-SUBN offers desirable data utility with low communication cost.

innovative
innovative Request Video

Package Features

  • 12/7 Support
  • Voice Conference
  • Video On Demand
  • Remote Connectivity
  • Customization
  • Live Chat Support
  • Toll Free Support

Includes

  • Complete Source Code
  • Complete Documentation
  • Complete Presentation Slides
  • Flow Diagram
  • Database File
  • Screenshots
  • Execution Procedure
  • Readme File
  • Addons
  • Video Tutorials

Leave Your Comment!

Your email address will not be published. Required fields are marked *

Latest Projects

Call us : (+91) 9030333433 / 08772261612
Mail us : takeoffstudentprojects@gmail.com
Mail us : info@takeoffprojects.com