 [2305.11192] TPMDP: Threshold Personalized Multi-party Differential Privacy via Optimal Gaussian Mechanism




























  








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Computer Science > Cryptography and Security


arXiv:2305.11192 (cs)
    




  [Submitted on 18 May 2023 (v1), last revised 30 May 2023 (this version, v3)]
Title:TPMDP: Threshold Personalized Multi-party Differential Privacy via Optimal Gaussian Mechanism
Authors:Jiandong Liu, Lan Zhang, Chaojie Lv, Ting Yu, Nikolaos M. Freris, Xiang-Yang Li View a PDF of the paper titled TPMDP: Threshold Personalized Multi-party Differential Privacy via Optimal Gaussian Mechanism, by Jiandong Liu and 5 other authors
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Abstract:In modern distributed computing applications, such as federated learning and AIoT systems, protecting privacy is crucial to prevent adversarial parties from colluding to steal others' private information. However, guaranteeing the utility of computation outcomes while protecting all parties' data privacy can be challenging, particularly when the parties' privacy requirements are highly heterogeneous. In this paper, we propose a novel privacy framework for multi-party computation called Threshold Personalized Multi-party Differential Privacy (TPMDP), which addresses a limited number of semi-honest colluding adversaries. Our framework enables each party to have a personalized privacy budget. We design a multi-party Gaussian mechanism that is easy to implement and satisfies TPMDP, wherein each party perturbs the computation outcome in a secure multi-party computation protocol using Gaussian noise. To optimize the utility of the mechanism, we cast the utility loss minimization problem into a linear programming (LP) problem. We exploit the specific structure of this LP problem to compute the optimal solution after O(n) computations, where n is the number of parties, while a generic solver may require exponentially many computations. Extensive experiments demonstrate the benefits of our approach in terms of low utility loss and high efficiency compared to existing private mechanisms that do not consider personalized privacy requirements or collusion thresholds.
    


 
Comments:
12 pages, 4 figures, submitted to MASS 2023, correct typos


Subjects:

Cryptography and Security (cs.CR); Multiagent Systems (cs.MA)

Cite as:
arXiv:2305.11192 [cs.CR]


 
(or 
arXiv:2305.11192v3 [cs.CR] for this version)
          
 
 

https://doi.org/10.48550/arXiv.2305.11192



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                arXiv-issued DOI via DataCite
              







Submission history From: Jiandong Liu [view email]       [v1]
        Thu, 18 May 2023 12:39:57 UTC (2,321 KB)
[v2]
        Mon, 22 May 2023 07:10:15 UTC (2,323 KB)
[v3]
        Tue, 30 May 2023 02:16:36 UTC (2,323 KB)



 

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