Venkat Arun (Massachusetts Institute of Technology), Aniket Kate (Purdue University), Deepak Garg (Max Planck Institute for Software Systems), Peter Druschel (Max Planck Institute for Software Systems), Bobby Bhattacharjee (University of Maryland)

For fear of retribution, the victim of a crime may be willing to report it only if other victims of the same perpetrator also step forward. Common examples include identifying oneself as the victim of sexual harassment by a person in a position of authority or accusing an influential politician, an authoritarian government or ones own employer of corruption. To handle such situations, legal literature has proposed the concept of an allegation escrow, a neutral third-party that collects allegations anonymously, matches allegations against each other, and de-anonymizes allegers only after de-anonymity thresholds (in terms of number of co-allegers), pre-specified by the allegers, are reached.

An allegation escrow can be realized as a single trusted third party; however, this party must be trusted to keep the identity of the alleger and content of the allegation private. To address this problem, this paper introduces Secure Allegation Escrows (SAE, pronounced ``say''). A SAE is a group of parties with independent interests and motives, acting jointly as an escrow for collecting allegations from individuals, matching the allegations, and de-anonymizing the allegations when designated thresholds are reached. By design, SAEs provide a very strong property: No less than a majority of parties constituting a SAE can de-anonymize or disclose the content of an allegation without a sufficient number of matching allegations (even in collusion with any number of other allegers). Once a sufficient number of matching allegations exist, all parties can simultaneously disclose the allegation with a verifiable proof of the allegers' identities. We describe how SAEs can be constructed using a novel authentication protocol and a novel allegation matching and bucketing algorithm, provide formal proofs of the security of our constructions, and provide an evaluation of a prototype implementation, demonstrating feasibility in practice.

View More Papers

Locally Differentially Private Frequency Estimation with Consistency

Tianhao Wang (Purdue University), Milan Lopuhaä-Zwakenberg (Eindhoven University of Technology), Zitao Li (Purdue University), Boris Skoric (Eindhoven University of Technology),...

Read More

Dynamic Searchable Encryption with Small Client Storage

Ioannis Demertzis (University of Maryland), Javad Ghareh Chamani (Hong Kong University of Science and Technology & Sharif University of Technology),...

Read More

Automated Discovery of Cross-Plane Event-Based Vulnerabilities in Software-Defined Networking

Benjamin E. Ujcich (University of Illinois at Urbana-Champaign), Samuel Jero (MIT Lincoln Laboratory), Richard Skowyra (MIT Lincoln Laboratory), Steven R....

Read More

CloudLeak: Large-Scale Deep Learning Models Stealing Through Adversarial Examples

Honggang Yu (University of Florida), Kaichen Yang (University of Florida), Teng Zhang (University of Central Florida), Yun-Yun Tsai (National Tsing...

Read More