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Self-Generating Policies for Machine Learning in Coalition Environments.

, , , , , , , , , and . PADG@ESORICS, volume 11550 of Lecture Notes in Computer Science, page 42-65. Springer, (2018)

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A Policy-based Approach for Measuring Data Quality., , , , and . IEEE BigData, page 4025-4031. IEEE, (2019)Policy Supersession in a Coalition ISR Network., , , , and . POLICY, page 178-179. IEEE Computer Society, (2011)Self-Generating Policies for Machine Learning in Coalition Environments., , , , , , , , , and . PADG@ESORICS, volume 11550 of Lecture Notes in Computer Science, page 42-65. Springer, (2018)Analyzing Federated Learning through an Adversarial Lens., , , and . ICML, volume 97 of Proceedings of Machine Learning Research, page 634-643. PMLR, (2019)Provenance-Based Analytics Services for Access Control Policies., , , , , , and . SERVICES, page 94-101. IEEE Computer Society, (2017)The Challenge of Access Control Policies Quality., , , , and . ACM J. Data Inf. Qual., 10 (2): 6:1-6:6 (2018)Analyzing Federated Learning through an Adversarial Lens., , , and . CoRR, (2018)Risks and Challenges of Training Classifiers for IoT., , and . ICIOT, volume 12993 of Lecture Notes in Computer Science, page 15-28. Springer, (2021)FLAP - A Federated Learning Framework for Attribute-based Access Control Policies., , , , , and . CODASPY, page 263-272. ACM, (2023)Policy based Ensembles for applying ML on Big Data., , , , and . IEEE BigData, page 4038-4044. IEEE, (2019)