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When the Signal is in the Noise: Exploiting Diffix's Sticky Noise.

, , , , and . USENIX Security Symposium, page 1081-1098. USENIX Association, (2019)

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When the signal is in the noise: The limits of Diffix's sticky noise., , , and . CoRR, (2018)The risk of re-identification remains high even in country-scale location datasets., , and . Patterns, 2 (3): 100204 (2021)Differentially Private Compressive K-means., , , , , and . ICASSP, page 7933-7937. IEEE, (2019)Synthetic Data - what, why and how?, , , , , , , and . CoRR, (2022)Pool Inference Attacks on Local Differential Privacy: Quantifying the Privacy Guarantees of Apple's Count Mean Sketch in Practice., , , and . USENIX Security Symposium, page 501-518. USENIX Association, (2022)Web Privacy: A Formal Adversarial Model for Query Obfuscation., , , and . IEEE Trans. Inf. Forensics Secur., (2023)A Framework for Auditable Synthetic Data Generation., , , , , , , and . CoRR, (2022)Pool Inference Attacks on Local Differential Privacy: Quantifying the Privacy Guarantees of Apple's Count Mean Sketch in Practice., , , and . CoRR, (2023)QuerySnout: Automating the Discovery of Attribute Inference Attacks against Query-Based Systems., , , and . CCS, page 623-637. ACM, (2022)When the Signal is in the Noise: Exploiting Diffix's Sticky Noise., , , , and . USENIX Security Symposium, page 1081-1098. USENIX Association, (2019)