Results 1 to 10 of about 12,304 (253)
A Statistical Threshold for Adversarial Classification in Laplace Mechanisms [PDF]
2021 IEEE Information Theory Workshop (ITW), 2021 This paper studies the statistical characterization of detecting an adversary who wants to harm some computation such as machine learning models or aggregation by altering the output of a differentially private mechanism in addition to discovering some information about the underlying dataset.
Ayse Unsal, Melek Önen
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The Bounded Laplace Mechanism in Differential Privacy
The Journal of Privacy and Confidentiality, 2019 The Laplace mechanism is the workhorse of differential privacy, applied to many instances where numerical data is processed. However, the Laplace mechanism can return semantically impossible values, such as negative counts, due to its infinite support ...
Naoise Holohan +3 more
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Results in Chemistry, 2023 The theoretical analysis of the diffusional electrode for EC' mechanism in the pseudo-first-order catalytic reaction is discussed. The exact, new, closed and compact form of the current profile is derived for this mechanism for all times and with all ...
Pandy Pirabaharan +2 more
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Differential Privacy via a Truncated and Normalized Laplace Mechanism
Journal of Computer Science and Technology, 2022 When querying databases containing sensitive information, the privacy of individuals stored in the database has to be guaranteed. Such guarantees are provided by differentially private mechanisms which add controlled noise to the query responses. However, most such mechanisms do not take into consideration the valid range of the query being posed. Thus,
Jörg-Rüdiger Sack
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Grafting Laplace and Gaussian Distributions: A New Noise Mechanism for Differential Privacy
IEEE Transactions on Information Forensics and Security, 2023 The framework of differential privacy protects an individual's privacy while publishing query responses on congregated data. In this work, a new noise addition mechanism for differential privacy is introduced where the noise added is sampled from a hybrid density that resembles Laplace in the centre and Gaussian in the tail.
Gokularam M, Sheetal Kalyani
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The Laplace Mechanism has optimal utility for differential privacy over continuous queries [PDF]
2021 36th Annual ACM/IEEE Symposium on Logic in Computer Science (LICS), 2021 Differential Privacy protects individuals' data when statistical queries are published from aggregated databases: applying "obfuscating" mechanisms to the query results makes the released information less specific but, unavoidably, also decreases its utility. Yet it has been shown that for discrete data (e.g.
Annabelle McIver +2 more
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Distributed electricity-carbon co-optimization of hydrogen energy storage microgrids with dynamic laplace perturbation and model predictive control. [PDF]
PLoS ONEHydrogen energy storage microgrids can improve renewable-energy accommodation and cross-energy flexibility, while repeated boundary-variable exchange in distributed scheduling may expose private operating information.
Zhongzheng Li +5 more
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Output stream analysis in a queueing model with working vacation mechanism as a power reduction strategy. [PDF]
PLoS ONEThe aim of this paper is to analyze the departure process of a queueing model with general independent input stream, exponentially distributed service time, finite buffer and working vacation mechanism.
Martyna Kobielnik, Wojciech M Kempa
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Hole-Free Differentially Private Multiparty Laplace Mechanism
SN Computer ScienceAbstract Differential privacy (DP) employs noise addition methods to protect individual data privacy. These methods integrate a controlled quantity of random noise into a database query response to obscure the presence of any specific individual in the dataset.
Amir Zarei, Staal A. Vinterbo
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Communication-Efficient Laplace Mechanism for Differential Privacy via Random Quantization
ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)We propose the first method that realizes the Laplace mechanism exactly (i.e., a Laplace noise is added to the data) that requires only a finite amount of communication (whereas the original Laplace mechanism requires the transmission of a real number) while guaranteeing privacy against the server and database.
Cheuk Ting Li
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