arXiv subjects

Malte Breuer

Publications and source records attributed to Malte Breuer.

5 recordsLinked to original sources

arXiv2024

Efficient Privacy-Preserving Approximation of the Kidney Exchange Problem

The kidney exchange problem (KEP) seeks to find possible exchanges among pairs of patients and their incompatible kidney donors while meeting specific optimization criteria such as maximizing the overall number of possible transplants. Recently, several privacy-preserving protocols for solving the KEP have been proposed. However, the protocols known to date lack scalability in practice since the KEP is an NP-complete problem. We address this issue by proposing a novel privacy-preserving protocol which computes an approximate solution for the KEP that scales well for the large numbers of patient-donor pairs encountered in practice. As opposed to prior work on privacy-preserving kidney exchange, our protocol is generic w.r.t.\ the security model that can be employed. Compared to the most efficient privacy-preserving protocols for kidney exchange existing to date, our protocol is entirely data oblivious and it exhibits a far superior run time performance. As a second contribution, we use a real-world data set to simulate the application of our protocol as part of a kidney exchange platform, where patient-donor pairs register and de-register over time, and thereby determine its approximation quality in a real-world setting.

cs.CR↗

arXiv2023

Solving the Kidney Exchange Problem Using Privacy-Preserving Integer Programming (Updated and Extended Version)

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 pairs 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 computing of the exchanges. However, the proposed approaches to date either only compute an approximative solution to the KEP or they suffer from a huge decrease in performance. In this paper, we suggest a novel privacy-preserving protocol that computes an exact solution to the KEP and significantly outperforms the other existing exact approaches. Our novel protocol is based on Integer Programming which is the most efficient method for solving the KEP in the non privacy-preserving case. We achieve an improved performance compared to the privacy-preserving approaches known to date by extending the output of the ideal functionality to include the termination decisions of the underlying algorithm. We implement our protocol in the SMPC benchmarking framework MP-SPDZ and compare its performance to the existing protocols for solving the KEP. In this extended version of our paper, we also evaluate whether and if so how much information can be inferred from the extended output of the ideal functionality.

cs.CR↗

arXiv2023

Introducing a Framework to Enable Anonymous Secure Multi-Party Computation in Practice (Extended Version)

Secure Multi-Party Computation (SMPC) allows a set of parties to securely compute a functionality in a distributed fashion without the need for any trusted external party. Usually, it is assumed that the parties know each other and have already established authenticated channels among each other. However, in practice the parties sometimes must stay anonymous. In this paper, we conceptualize a framework that enables the repeated execution of an SMPC protocol for a given functionality such that the parties can keep their participation in the protocol executions private and at the same time be sure that only authorized parties may take part in a protocol execution. We identify the security properties that an implementation of our framework must meet and introduce a first implementation of the framework that achieves these properties.

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arXiv2022

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

To this day, there are still some countries where the exchange of kidneys between multiple incompatible patient-donor pairs is restricted by law. Typically, legal regulations in this context are put in place to prohibit coercion and manipulation in order to prevent a market for organ trade. Yet, in countries where kidney exchange is practiced, existing platforms to facilitate such exchanges generally lack sufficient privacy mechanisms. In this paper, we propose a privacy-preserving protocol for kidney exchange that not only addresses the privacy problem of existing platforms but also is geared to lead the way in overcoming legal issues in those countries where kidney exchange is still not practiced. In our approach, we use the concept of secret sharing to distribute the medical data of patients and donors among a set of computing peers in a privacy-preserving fashion. These computing peers then execute our new Secure Multi-Party Computation (SMPC) protocol among each other to determine an optimal set of kidney exchanges. As part of our new protocol, we devise a privacy-preserving solution to the maximum matching problem on general graphs. We have implemented the protocol in the SMPC benchmarking framework MP-SPDZ and provide a comprehensive performance evaluation. Furthermore, we analyze the practicality of our protocol when used in a dynamic setting (where patients and donors arrive and depart over time) based on a data set from the United Network for Organ Sharing.

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arXiv2020

A Privacy-Preserving Protocol for the Kidney Exchange Problem

Kidney donations from living donors form an attractive alternative to long waiting times on a list for a post-mortem donation. However, even if a living donor for a given patient is found, the donor's kidney might not meet the patient's medical requirements. If several patients are in this position, they may be able to exchange donors in a cyclic fashion. Current algorithmic approaches for determining such exchange cycles neglect the privacy requirements of donors and patients as they require their medical data to be centrally collected and evaluated. In this paper, we present the first distributed privacy-preserving protocol for kidney exchange that ensures the correct computing of the exchange cycles while at the same time protecting the privacy of the patients' sensitive medical data. We prove correctness and security of the new protocol and evaluate its practical performance.

cs.CR↗