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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| Vydáno v: | IEEE transactions on information theory Ročník 64; číslo 8; s. 5677 - 5690 |
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| Médium: | Journal Article |
| Jazyk: | angličtina |
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IEEE
01.08.2018
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
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| ISSN: | 0018-9448, 1557-9654 |
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| Abstract | 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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| AbstractList | 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. 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 (E, δ)-data-privacy, defined as the probability of E-accurate estimate (the difference of an estimation and the original data is within E) is no larger than δ (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. |
| Author | Cai, Lin Guan, Xinping He, Jianping |
| Author_xml | – sequence: 1 givenname: Jianping orcidid: 0000-0002-6253-7802 surname: He fullname: He, Jianping email: jianpinghe.zju@gmail.com organization: Department of Automation, Shanghai Jiao Tong University, Shanghai, China – sequence: 2 givenname: Lin orcidid: 0000-0002-1093-4865 surname: Cai fullname: Cai, Lin email: cai@ece.uvic.ca organization: Department of Electrical and Computer Engineering, University of Victoria, BC, Canada – sequence: 3 givenname: Xinping orcidid: 0000-0003-1858-8538 surname: Guan fullname: Guan, Xinping email: xpguan@sjtu.edu.cn organization: Department of Automation, Shanghai Jiao Tong University, Shanghai, China |
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| SubjectTerms | 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 |
| Title | Preserving Data-Privacy With Added Noises: Optimal Estimation and Privacy Analysis |
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