Demonstration venue · read-only. Every page can be browsed; the buttons that would change it are switched off. Create an account to run TaxoReview on your own data. A simulation from public publication records: the people listed were not invited and did not agree to review.
? Tutorial for this page

Matthew Gregoire

dblp:321/4200 · DBLP profile ↗
← Back
2ranked-venue papers
2as first author
2since 2021 · last 2025
—ORCID · unresolved

Domains — the database's venue-derived domains; a paper can count in several

Security and privacy · 2 · 2 first-author · 2 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Network and information security
1 paper
Network security · 70% Usable security · 30%

Topics — the 4 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Usable security › security operations
abuse reporting
0.912025
Onion Franking: Abuse Reports for Mix-Based Private Messaging · NDSS 2025
Network security
anonymity networks
0.912025
Onion Franking: Abuse Reports for Mix-Based Private Messaging · NDSS 2025
Network security › anonymity networks
mix networks
0.912025
Onion Franking: Abuse Reports for Mix-Based Private Messaging · NDSS 2025
Network security › anonymity networks
anonymous communication
0.312025
Onion Franking: Abuse Reports for Mix-Based Private Messaging · NDSS 2025

Methods — techniques the papers use, named apart from their topics

franking · 0.9
YearPublicationVenuePosition
2025 Onion Franking: Abuse Reports for Mix-Based Private Messaging
Matthew Gregoire, Margaret Pierce, Saba Eskandarian
NDSS1
2024 CheckOut: User-Controlled Anonymization for Customer Loyalty Programs
abstract
To resist the regimes of ubiquitous surveillance imposed upon us in every facet of modern life, we need technological tools that subvert surveillance systems. Unfortunately, while cryptographic tools frequently demonstrate how we can construct systems that safeguard user privacy, there is limited motivation for corporate entities engaged in surveillance to adopt these tools, as they often clash with profit incentives. This paper demonstrates how, in one particular aspect of everyday life -- customer loyalty programs -- users can subvert surveillance and attain anonymity, without necessitating any cooperation or modification in the behavior of their surveillors. We present the CheckOut system, which allows users to coordinate large anonymity sets of shoppers to hide the identity and purchasing habits of each particular user in the crowd. CheckOut scales up and systematizes past efforts to subvert loyalty surveillance, which have been primarily ad-hoc and manual affairs where customers physically swap loyalty cards to mask their real identities. CheckOut allows increased scale while ensuring that the necessary computing infrastructure does not itself become a new centralized point of privacy failure. Of particular importance to our scheme is a protocol for loyalty programs that offer reward points, where we demonstrate how CheckOut can assist users in paying each other back for loyalty points accrued while using each others' loyalty accounts. We present two different mechanisms to facilitate redistributing rewards points, offering trade-offs in functionality, performance, and security.
Matthew Gregoire, Rachel Thomas, Saba Eskandarian
Proc. Priv. Enhancing Technol.1