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ML-Doctor: Holistic Risk Assessment of Inference Attacks Against Machine Learning Models.

, , , , , , , , and . USENIX Security Symposium, page 4525-4542. USENIX Association, (2022)

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REAP: An Efficient Incentive Mechanism for Reconciling Aggregation Accuracy and Individual Privacy in Crowdsensing., , , and . CoRR, (2017)AHEAD: Adaptive Hierarchical Decomposition for Range Query under Local Differential Privacy., , , , , , and . CCS, page 1266-1288. ACM, (2021)CampER: An Effective Framework for Privacy-Aware Deep Entity Resolution., , , , , , , and . KDD, page 626-637. ACM, (2023)Cardinality constraint analyse and implementation in access control model., , and . NCM, page 265-267. IEEE Computer Society, (2010)DPAdapter: Improving Differentially Private Deep Learning through Noise Tolerance Pre-training., , , , , , and . USENIX Security Symposium, USENIX Association, (2024)The Campus Surveying Control Network Digital Construction Planning and Implementation., , , and . Geoinformatics, page 1-5. IEEE, (2018)DPSyn: Experiences in the NIST Differential Privacy Data Synthesis Challenges., , and . CoRR, (2021)On the Privacy Risks of Cell-Based NAS Architectures., , , , , and . CCS, page 1427-1441. ACM, (2022)Continuous Release of Data Streams under both Centralized and Local Differential Privacy., , , , , , , and . CCS, page 1237-1253. ACM, (2021)Achieving Bilateral Utility Maximization and Location Privacy Preservation in Database-Driven Cognitive Radio Networks., , , and . MASS, page 181-189. IEEE Computer Society, (2015)