Shield: Secure Allegation Escrow System with Stronger Guarantees
Nishat Koti, Varsha Bhat Kukkala, Arpita Patra, Bhavish Raj Gopal
摘要
The rising issues of harassment, exploitation, corruption and other forms of abuse have led victims to seek comfort by acting in unison against common perpetrators. This is corroborated by the widespread #MeToo movement, which was explicitly against sexual harassment. Installation of escrow systems has allowed victims to report such incidents. The escrows are responsible for identifying the perpetrator and taking the necessary action to bring justice to all its victims. However, users hesitate to participate in these systems due to the fear of such sensitive reports being leaked to perpetrators, who may further misuse them. Thus, to increase trust in the system, cryptographic solutions are being designed to realize web-based secure allegation escrow (SAE) systems. While the work of Arun et al. (NDSS’20) presents the state-of-the-art solution, we identify attacks that can leak sensitive information and compromise victim privacy. We also report issues present in prior works that were left unidentified. Having identified the attacks and issues in all prior works, we put forth an SAE system that overcomes these while retaining all the existing salient features. The cryptographic technique of secure multiparty computation (MPC) serves as the primary underlying tool in designing our system. At the heart of our system lies a new duplicity check protocol and an improved matching protocol. We also provide essential features such as allegation modification and deletion, which were absent in the state of the art. To demonstrate feasibility, we benchmark the proposed system with state-of-the-art MPC protocols and report the cost of processing an allegation. Different settings that affect system performance are analyzed, and the reported values showcase the practicality of our solution.
问问这篇 Paper
问问你的智能体。
Lune 读过与它相关的顶会 Paper,每个回答都会注明依据哪几篇。
相关 Paper
- Finding Safety in Numbers with Secure Allegation EscrowsVenkat Arun, Aniket Kate, Deepak Garg, Peter Druschel 等NDSS 2020
- An End-to-End System for Large Scale P2P MPC-as-a-Service and Low-Bandwidth MPC for Weak ParticipantsAssi Barak, Martin Hirt, Lior Koskas, Yehuda LindellCCS 2018 · 被引用 52 次
- C rypt D ough : A Unified Analytics Engine for Secure Multiparty ComputationMuhammad Faisal, Alessandra Lanz, Sam Buxbaum, Adam Godel 等SOSP 2026
- Senate: A Maliciously-Secure MPC Platform for Collaborative AnalyticsRishabh Poddar, Sukrit Kalra, Avishay Yanai, Ryan Deng 等USENIX Security 2021 · 被引用 89 次
- MCMix: Anonymous Messaging via Secure Multiparty ComputationNikolaos Alexopoulos, Aggelos Kiayias, Riivo Talviste, Thomas ZachariasUSENIX Security 2017 · 被引用 85 次
