Award Abstract # 1646999
EAGER: Exploring the Use of Secure Multi-Party Computation in the Context of Organ Donation

NSF Org: CCF
Division of Computing and Communication Foundations
Recipient: THE TRUSTEES OF THE STEVENS INSTITUTE OF TECHNOLOGY
Initial Amendment Date: July 21, 2016
Latest Amendment Date: August 26, 2020
Award Number: 1646999
Award Instrument: Standard Grant
Program Manager: Tracy Kimbrel
CCF
 Division of Computing and Communication Foundations
CSE
 Directorate for Computer and Information Science and Engineering
Start Date: September 1, 2016
End Date: August 31, 2021 (Estimated)
Total Intended Award Amount: $125,669.00
Total Awarded Amount to Date: $125,669.00
Funds Obligated to Date: FY 2016 = $125,669.00
History of Investigator:
  • Susanne Wetzel (Principal Investigator)
    swetzel@stevens.edu
  • Robert Gilman (Former Principal Investigator)
  • Susanne Wetzel (Former Principal Investigator)
  • Giuseppe Ateniese (Former Principal Investigator)
Recipient Sponsored Research Office: Stevens Institute of Technology
ONE CASTLE POINT ON HUDSON
HOBOKEN
NJ  US  07030-5906
(201)216-8762
Sponsor Congressional District: 08
Primary Place of Performance: Stevens Institute of Technology
Castle Point on Hudson
Hoboken
NJ  US  07030-5991
Primary Place of Performance
Congressional District:
08
Unique Entity Identifier (UEI): JJ6CN5Y5A2R5
Parent UEI:
NSF Program(s): Secure &Trustworthy Cyberspace
Primary Program Source: 01001617DB NSF RESEARCH & RELATED ACTIVIT
Program Reference Code(s): 7434, 7916, 9102
Program Element Code(s): 806000
Award Agency Code: 4900
Fund Agency Code: 4900
Assistance Listing Number(s): 47.070

ABSTRACT

Informally speaking, Secure Multi-Party Computation (SMPC) allows two or more parties to jointly compute some function on their private inputs in a distributed fashion (i.e., without the involvement of a trusted third party) such that none of the parties learns anything beyond its dedicated output and what it can deduce from considering both this output and its own private input. Since its inception in 1982 by Yao, SMPC has advanced greatly and over the years a large body of work has been developed. To date, prominent applications for SMPC include private set intersection, auctions, and data mining. However, despite all advances, there still are many areas of application for which the use of SMPC has not yet been explored. Considering the fact that SMPC allows one to achieve strong security guarantees, the use of SMPC should be further advanced into fields of application which require the handling of highly-sensitive information of multiple parties in a centralized fashion and as such exhibit great promise to substantially benefit from the use of SMPC techniques. Such an area of application is organ donation. Currently, more than 120,000 patients in the U.S. alone are waiting to receive a lifesaving organ transplant and the need by far outweighs the number of available organs. Increasing the pool of organ donors is challenging and reports of organ scandals have even resulted in a decline in the number of potential organ donors. On one hand, transparency and fairness in the allocation process was shown to influence the willingness to donate organs. In turn, it is argued that in the case of living donations (where a patient has a willing donor but the donor's medical characteristics are not compatible with those of the patient), the recipient of the organ donation should have the right for the transparency to be limited. As such this project seeks to explore whether it is possible to effectively and efficiently introduce SMPC into the context of organ donation with the goal to ensure suitable transparency and privacy guarantees for donors and recipients alike. The potential impact of this work is substantial---for individual patients and society at large---in that addressing common attacks on traditional organ donation systems may not only help rebuild lost trust but may even lead to a greater buy-in than ever before.

For living donations, the project seeks to devise initial protocols which allow the determination of donors in a cyclic fashion such that (a) it does not require a trusted third party, (b) the attributes of all patients and donors are kept private at all times, (c) all parties are satisfied with the exchange, (d) application-specific requirements are met, and (e) it is secure even in the presence of adversaries. For post-mortem donations, the project will explore the suitability of traditional privacy-preserving matching approaches---recognizing that matching the characteristics of organs come with unique challenges and requirements. Also, the project will investigate whether it is feasible to introduce a systemic change to how post-mortem organ donation is carried out today.

PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH

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Wueller, Stefan and Breuer, Malte and Meyer, Ulrike and Wetzel, Susanne "Privacy-Preserving Trade Chain Detection" Data Privacy Management Workshop , 2018 10.1007/978-3-030-00305-0_26 Citation Details

PROJECT OUTCOMES REPORT

Disclaimer

This Project Outcomes Report for the General Public is displayed verbatim as submitted by the Principal Investigator (PI) for this award. Any opinions, findings, and conclusions or recommendations expressed in this Report are those of the PI and do not necessarily reflect the views of the National Science Foundation; NSF has not approved or endorsed its content.

Informally speaking, Secure Multi-Party Computation (SMPC) allows two or more parties to jointly compute some function on their private inputs in a distributed fashion (i.e., without the involvement of a trusted third party) such that none of the parties learns anything beyond its dedicated output and what it can deduce from considering both this output and its own private input. The purpose of this work was to explore whether it is possible to effectively and efficiently introduce SMPC into the context of organ donation with the goal to ensure suitable transparency and privacy guarantees for donors and recipients alike. The potential impact of this work is substantial---for individual patients and society at large---in that addressing common attacks on traditional organ donation systems may not only help rebuild lost trust but may even lead to a greater buy-in than ever before.

With our work we were able to show that it is in fact possible to use SMPC to devise privacy-preserving protocols to solve the so-called kidney exchange problem. The kidney exchange problem is an optimization problem that seeks to determine exchanges amongst incompatible patient-donor pairs such that the number of patients that can receive a kidney transplant is maximized. The main results of our work include a privacy-preserving protocol to compute exchange cycles as well as a privacy-preserving protocol for crossover exchanges (to solve the kidney exchange problem). Some of our results are of independent interest beyond the use case of kidney exchange, including a privacy-preserving protocol to solve the maximum matching problem on general graphs.

This work was carried out in collaboration with the group of Professor Ulrike Meyer at RWTH Aachen University, independently funded under DFG grant 419340256.


Last Modified: 06/26/2022
Modified by: Susanne Wetzel

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