Privacy Preservation in Distributed Optimization via Dual Decomposition and ADMM

Tjell, Katrine; Wisniewski, Rafael

doi:10.1109/cdc40024.2019.9028969

Cited by 13 publications

(10 citation statements)

References 17 publications

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“…The eavesdropping adversary is assumed to attack the system by listening to the messages transmitted along the communication channels, i.e., edges. This model receives little attention as it can be addressed by assuming all communication channels are securely encrypted such that the transmitted messages cannot be eavesdropped, e.g., secret sharing based approaches [10], [11], [24]. However, this assumption is particularly expensive to realize in distributed optimization applications, as a large number of iterations is often required.…”

Section: B Adversary Modelmentioning

confidence: 99%

“…Additionally, it is hard to achieve differential privacy in practice. The second class is that of secret-sharing based distributed optimization approaches [10], [11] which deploy secret sharing to prevent privacy leakage, a technique used in secure multiparty computation [12], [13]. Secret sharing works by splitting the private data into a number of so-called shares and distributes them over the nodes such that without a sufficient number of nodes cooperating the private data cannot be reconstructed.…”

Section: Introductionmentioning

confidence: 99%

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Communication efficient privacy-preserving distributed optimization using adaptive differential quantization

Li¹,

Heusdens²,

Christensen³

2021

Preprint

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Privacy issues and communication cost are both major concerns in distributed optimization. There is often a trade-off between them because the encryption methods required for privacy-preservation often incur expensive communication bandwidth. To address this issue, we, in this paper, propose a quantization-based approach to achieve both communication efficient and privacy-preserving solutions in the context of distributed optimization. By deploying an adaptive differential quantization scheme, we allow each node in the network to achieve its optimum solution with a low communication cost while keeping its private data unrevealed. Additionally, the proposed approach is general and can be applied in various distributed optimization methods, such as the primal-dual method of multipliers (PDMM) and the alternating direction method of multipliers (ADMM). Moveover, we consider two widely used adversary models: passive and eavesdropping. Finally, we investigate the properties of the proposed approach using different applications and demonstrate its superior performance in terms of several parameters including accuracy, privacy, and communication cost.

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Section: B Adversary Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Communication efficient privacy-preserving distributed optimization using adaptive differential quantization

Li¹,

Heusdens²,

Christensen³

2021

Preprint

View full text Add to dashboard Cite

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“…Many information-theoretic approaches have been proposed for addressing privacy issues in various distributed processing problems like distributed average consensus [4]- [16], distributed least squares [17], [18], distributed optimization [19]- [27] and distributed graph filtering [28]. These approaches can be broadly classified into three classes.…”

Section: A Related Workmentioning

confidence: 99%

Privacy-Preserving Distributed Processing: Metrics, Bounds, and Algorithms

Gundersen

Heusdens

et al. 2020

Preprint

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Privacy-preserving distributed processing has recently attracted considerable attention. It aims to design solutions for conducting signal processing tasks over networks in a decentralized fashion without violating privacy. Many algorithms can be adopted to solve this problem such as differential privacy, secure multiparty computation, and the recently proposed distributed optimization based subspace perturbation. However, how these algorithms relate to each other is not fully explored yet. In this paper, we therefore first propose information-theoretic metrics based on mutual information. Using the proposed metrics, we are able to compare and relate a number of existing wellknown algorithms. We then derive a lower bound on individual privacy that gives insights on the nature of the problem. To validate the above claims, we investigate a concrete example and compare a number of state-of-the-art approaches in terms of different aspects such as output utility, individual privacy and algorithm robustness against the number of corrupted parties, using not only theoretical analysis but also numerical validation. Finally, we discuss and provide principles for designing appropriate algorithms for different applications.

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“…So far, efficient data processing and privacy preservation are two terms that seems difficult to combine since the cryptographic methods tend to bring a substantial overhead in either communication, computation or both. Moreover, security of cryptographic methods such as secret sharing and homomorphic encryption relies on modular arithmetic, which entails that all data to be protected must be integers and computations on this data must be translated into equivalent computations using finite field arithmetic, [4,5,6]. The drawbacks of this are, for instance, loss of precision in the solution (because of rounding decimal numbers to integers) and that many operations such as division becomes very intractable.…”

Section: Introductionmentioning

confidence: 99%

Privacy in Distributed Computations based on Real Number Secret Sharing

Tjell¹,

Wiśniewski²

2021

Preprint

Self Cite

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Privacy preservation in distributed computations is an important subject as digitization and new technologies enable collection and storage of vast amounts of data, including private data belonging to individuals. To this end, there is a need for a privacy preserving computation framework that minimises the leak of private information during computations while being efficient enough for practical usage. This paper presents a step towards such a framework with the proposal of a real number secret sharing scheme that works directly on real numbers without the need for conversion to integers which is the case in related schemes. The scheme offers computations like addition, multiplication, and division to be performed directly on secret shared data (the cipher text version of the data). Simulations show that the scheme is much more efficient in terms of accuracy than its counterpart version based on integers and finite field arithmetic. The drawback with the proposed scheme is that it is not perfectly secure. However, we provide a privacy analysis of the scheme, where we show that the leaked information can be upper bounded and asymptotically goes to zero. To demonstrate the scheme, we use it to perform Kalman filtering directly on secret shared data.

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Privacy Preservation in Distributed Optimization via Dual Decomposition and ADMM

Cited by 13 publications

References 17 publications

Communication efficient privacy-preserving distributed optimization using adaptive differential quantization

Communication efficient privacy-preserving distributed optimization using adaptive differential quantization

Privacy-Preserving Distributed Processing: Metrics, Bounds, and Algorithms

Privacy in Distributed Computations based on Real Number Secret Sharing

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