Privacy-Preserving Maximum Matching on General Graphs and its Application to Enable Privacy-Preserving Kidney Exchange

Malte, Breuer,; Meyer, Ulrike; Wetzel, Susanne

doi:10.1145/3508398.3511509

Cited by 9 publications

(51 citation statements)

References 24 publications

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“…Our privacy-preserving KEP protocol SPIKE offers practical runtimes for real-world deployment. Our runtimes significantly outperform the measured runtimes of previous works [8,9], e.g., by a factor of hundreds/thousands for 9 recipient-donor pairs with a cycle length of 2. This is due to an efficient symbiosis of three MPC techniques and protocol optimizations that we will detail in the next section.…”

Section: Robust Kepmentioning

confidence: 64%

“…• Efficient Privacy-Preserving Kidney Exchange protocol: We design and implement SPIKE, an efficient, distributed, privacy-preserving protocol for solving the kidney exchange problem in the semi-honest security model. In contrast to the current state-of-the-art [8,9], SPIKE improves the medical compatibility matching by considering additional factors, namely, age, sex, human leukocyte antigens, and weight, that significantly affect compatibility between potential…”

Section: Contributions and Outlinementioning

confidence: 99%

“…Privacy-preserving KEP Just two works, both by Breuer et al [8,9], investigate how to solve the kidney exchange problem in a decentralized privacy-preserving manner. Both consider the semi-honest security model.…”

Section: Robust Kepmentioning

confidence: 99%

“…Privacy-preserving KEP with Shamir's Secret Sharing. In a concurrent work to ours, Breuer et al [9] introduced a privacy-preserving KEP protocol for crossover kidney exchanges with polynomial computation complexity. "Crossover" hereby means that the kidney exchange is done among two pairs, i.e., the exchange cycle size is limited to two in contrast to [8].…”

Section: Robust Kepmentioning

confidence: 99%

“…Furthermore, the semi-honest security model enables significantly more efficient computation than the malicious model and, hence, provides a good efficiency-privacy trade-off. Previous works on privacy-preserving KEP [8,9] are also in the semihonest security model.…”

Section: Security Modelmentioning

confidence: 99%

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SPIKE: Secure and Private Investigation of the Kidney Exchange problem

Birka¹,

Hamacher²,

Kussel³

et al. 2022

Preprint

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Background:The kidney exchange problem (KEP) addresses the matching of patients in need for a replacement organ with compatible living donors. Ideally many medical institutions should participate in a matching program to increase the chance for successful matches. However, to fulfill legal requirements current systems use complicated policy-based data protection mechanisms that effectively exclude smaller medical facilities to participate. Employing secure multi-party computation (MPC) techniques provides a technical way to satisfy data protection requirements for highly sensitive personal health information while simultaneously reducing the regulatory burdens. Results:We have designed, implemented, and benchmarked SPIKE, a secure MPC-based privacy-preserving KEP which computes a solution by finding matching donor-recipient pairs in a graph structure. SPIKE matches 40 pairs in cycles of length 2 in less than 4 minutes and outperforms the previous state-of-the-art protocol by a factor of 400× in runtime while providing medically more robust solutions. Conclusions:We show how to solve the KEP in a robust and privacy-preserving manner achieving practical performance. The usage of MPC techniques fulfills many data protection requirements on a technical level, allowing smaller health care providers to directly participate in a kidney exchange with reduced legal processes.

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Section: Robust Kepmentioning

confidence: 64%

Section: Contributions and Outlinementioning

confidence: 99%

Section: Robust Kepmentioning

confidence: 99%

Section: Robust Kepmentioning

confidence: 99%

Section: Security Modelmentioning

confidence: 99%

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SPIKE: Secure and Private Investigation of the Kidney Exchange problem

Birka¹,

Hamacher²,

Kussel³

et al. 2022

Preprint

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Solving the Kidney Exchange Problem Using Privacy-Preserving Integer Programming

Malte

Hein

Pompe

et al. 2022

2022 19th Annual International Conference on Privacy, Security &Amp; Trust (PST)

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The kidney exchange problem (KEP) is to find a constellation of exchanges that maximizes the number of transplants that can be carried out for a set of patients with kidney disease and their incompatible donors. Recently, this problem has been tackled from a privacy perspective in order to protect the sensitive medical data of patients and donors and to decrease the potential for manipulation of the computed exchanges. However, the proposed approaches either do not provide the same functionality as the conventional solutions to the KEP or they come along with a huge performance impact. In this paper, we provide a novel privacy-preserving protocol for the KEP which significantly outperforms the existing approaches by allowing a small information leakage. This leakage allows us to base our protocol on Integer Programming which is the most efficient method for solving the KEP in the non privacypreserving case. We implement our protocol in the SMPC benchmarking framework MP-SPDZ and compare its performance to the existing protocols for solving the KEP.

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