On the Utility Gain of Iterative Bayesian Update for Locally Differentially Private Mechanisms-Reference-Cited by-同舟云学术

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On the Utility Gain of Iterative Bayesian Update for Locally Differentially Private Mechanisms

Published:2023 Issue: Volume: Page:165-183
ISSN:0302-9743
Container-title:Data and Applications Security and Privacy XXXVII
language:
Short-container-title:

Author:

Arcolezi Héber H.^ORCID,Cerna Selene^ORCID,Palamidessi Catuscia^ORCID

Publisher

Springer Nature Switzerland

Link

https://link.springer.com/content/pdf/10.1007/978-3-031-37586-6_11

Reference30 articles.

1. Agrawal, D., Aggarwal, C.C.: On the design and quantification of privacy preserving data mining algorithms. In: Proceedings of the Twentieth ACM SIGMOD-SIGACT-SIGART Symposium on Principles of Database Systems. ACM (2001). https://doi.org/10.1145/375551.375602

2. Agrawal, R., Srikant, R., Thomas, D.: Privacy preserving OLAP. In: Proceedings of the 2005 ACM SIGMOD International Conference on Management of Data. ACM (2005). https://doi.org/10.1145/1066157.1066187

3. Arcolezi, H.H., Couchot, J.F., Bouna, B.A., Xiao, X.: Improving the utility of locally differentially private protocols for longitudinal and multidimensional frequency estimates. Digit. Commun. Netw. (2022). https://doi.org/10.1016/j.dcan.2022.07.003

4. Arcolezi, H.H., Couchot, J.F., Gambs, S., Palamidessi, C., Zolfaghari, M.: Multi-Freq-LDPy: multiple frequency estimation under local differential privacy in python. In: Atluri, V., Di Pietro, R., Jensen, C.D., Meng, W. (eds.) Computer Security - ESORICS 2022. Lecture Notes in Computer Science, vol. 13556, pp. 770–775. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-17143-7_40

5. Arcolezi, H.H., Gambs, S., Couchot, J.F., Palamidessi, C.: On the risks of collecting multidimensional data under local differential privacy. Proc. VLDB Endow. 16(5), 1126–1139 (2023). https://doi.org/10.14778/3579075.3579086

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