Preserving Data-Privacy With Added Noises: Optimal Estimation and Privacy Analysis

Network systems often rely on distributed algorithms to achieve a global computation goal with iterative local information exchanges between neighbor nodes. To preserve data privacy, a node may add a random noise to its original data for information exchange at each iteration. Nevertheless, an eaves...

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Bibliographic Details
Published in:IEEE transactions on information theory Vol. 64; no. 8; pp. 5677 - 5690
Main Authors: He, Jianping, Cai, Lin, Guan, Xinping
Format: Journal Article
Language:English
Published: New York IEEE 01.08.2018
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Algorithms
average consensus
Computer privacy
Data analysis
Data exchange
Data privacy
Distributed algorithm
Distributed algorithms
Distributed databases
distributed estimation
Eavesdropping
Estimation
Iterative methods
Nickel
noise adding mechanism
Optimization
Privacy
Random noise
Random variables
ISSN:0018-9448, 1557-9654
Online Access:Get full text
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Summary:Network systems often rely on distributed algorithms to achieve a global computation goal with iterative local information exchanges between neighbor nodes. To preserve data privacy, a node may add a random noise to its original data for information exchange at each iteration. Nevertheless, an eavesdropping node can estimate other's original data based on the information it received. The estimation accuracy and data privacy can be measured in terms of <inline-formula> <tex-math notation="LaTeX">(\epsilon, \delta) </tex-math></inline-formula>-data-privacy, defined as the probability of <inline-formula> <tex-math notation="LaTeX">\epsilon </tex-math></inline-formula>-accurate estimate (the difference of an estimation and the original data is within <inline-formula> <tex-math notation="LaTeX">\epsilon </tex-math></inline-formula>) is no larger than <inline-formula> <tex-math notation="LaTeX">\delta </tex-math></inline-formula> (the disclosure probability). How to optimize the estimation and analyze data privacy is a critical and open issue. In this paper, a theoretical framework is developed to investigate how to optimize the estimation of neighbor's original data using the local information received, named optimal distributed estimation. Then, we study the disclosure probability under the optimal estimation for data privacy analysis. We further apply the developed framework to analyze the data privacy of the privacy-preserving average consensus algorithm and identify the optimal noises for the algorithm.
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ISSN:0018-9448
1557-9654
DOI:10.1109/TIT.2018.2842221