TRUESET: Faster Verifiable Set Computations. - Ahmed E. Kosba; Dimitrios Papadopoulos 001; Charalampos Papamanthou; Mahmoud F. Sayed; Elaine Shi; Nikos Triandopoulos (USENIX Security Symposium, 2014)

TRUESET: Nearly Practical Verifiable Set Computations. - Ahmed E. Kosba; Dimitrios Papadopoulos 001; Charalampos Papamanthou; Mahmoud F. Sayed; Elaine Shi; Nikos Triandopoulos (IACR Cryptol. ePrint Arch., 2014)

Jointly Modeling Relevance and Sensitivity for Search Among Sensitive Content. - Mahmoud F. Sayed; Douglas W. Oard (SIGIR, 2019)

FACTS-IR: fairness, accountability, confidentiality, transparency, and safety in information retrieval. - Alexandra Olteanu; Jean Garcia-Gathright; Maarten de Rijke; Michael D. Ekstrand; Adam Roegiest; Aldo Lipani; Alex Beutel; Ana Lucic; Ana-Andreea Stoica; Anubrata Das 001; Asia Biega; Bart Voorn; et al. (SIGIR Forum, 2019)

A Test Collection for Relevance and Sensitivity. - Mahmoud F. Sayed; William Cox; Jonah Lynn Rivera; Caitlin Christian-Lamb; Modassir Iqbal; Douglas W. Oard; Katie Shilton (SIGIR, 2020)

The University of Maryland at the TREC 2020 Fair Ranking Track. - Mahmoud F. Sayed; Douglas W. Oard (TREC, 2020)

Search with Discretion: Value Sensitive Design of Training Data for Information Retrieval. - Modassir Iqbal; Katie Shilton; Mahmoud F. Sayed; Douglas W. Oard; Jonah Lynn Rivera; William Cox (Proc. ACM Hum. Comput. Interact., 2021)

Search Among Sensitive Content. - Mahmoud F. Sayed (2021)

Comparing Intrinsic and Extrinsic Evaluation of Sensitivity Classification. - Mahmoud F. Sayed; Nishanth Mallekav; Douglas W. Oard (ECIR (2), 2022)

Providing More Efficient Access to Government Records: A Use Case Involving Application of Machine Learning to Improve FOIA Review for the Deliberative Process Privilege. - Jason R. Baron; Mahmoud F. Sayed; Douglas W. Oard (ACM Journal on Computing and Cultural Heritage, 2022)