The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
No free lunch in data privacy
Abstract
Differential privacy is a powerful tool for providing privacy-preserving noisy query answers over statistical databases. It guarantees that the distribution of noisy query answers changes very little with the addition or deletion of any tuple. It is frequently accompanied by popularized claims that it provides privacy without any assumptions about the data and that it protects against attackers who know all but one record. In this paper we critically analyze the privacy protections offered by differential privacy.
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Citations
Cites
Calibrating Noise to Sensitivity in Private Data Analysis
Dwork, C., McSherry, F., Nissim, K., & Smith, A. (2006) · Lecture Notes in Computer Science · doi:10.1007/11681878_14 · Cited by 7,272
Our Data, Ourselves: Privacy Via Distributed Noise Generation
Dwork, C., Kenthapadi, K., McSherry, F., Mironov, I., & Naor, M. (2006) · Lecture Notes in Computer Science · doi:10.1007/11761679_29 · Cited by 1,835
Cited by
RAPPOR: Randomized Aggregatable Privacy-Preserving Ordinal Response
Erlingsson, Ú., Pihur, V., & Korolova, A. (2014) · Proceedings of the 2014 ACM SIGSAC Conference on Computer and Communications Security · doi:10.1145/2660267.2660348 · Cited by 1,838
A Survey on Security Threats and Defensive Techniques of Machine Learning: A Data Driven View
Liu, Q., Li, P., Zhao, W., Cai, W., Yu, S., & Leung, V. C. M. (2018) · IEEE Access · doi:10.1109/access.2018.2805680 · Cited by 421
Differential Privacy Preserving of Training Model in Wireless Big Data with Edge Computing
Du, M., Wang, K., Xia, Z., & Zhang, Y. (2020) · IEEE Transactions on Big Data · doi:10.1109/tbdata.2018.2829886 · Cited by 111
Privacy-Preserving Adversarial Networks
Tripathy, A., Wang, Y., & Ishwar, P. (2019) · 2019 57th Annual Allerton Conference on Communication, Control, and Computing (Allerton) · doi:10.1109/allerton.2019.8919758 · Cited by 87
Foundation Models and Fair Use
Henderson, P., Li, X., Jurafsky, D., Hashimoto, T., Lemley, M. A., & Liang, P. (2023) · doi:10.2139/ssrn.4404340 · Cited by 57
Metadata
- Authors
- Kifer, D., & Machanavajjhala, A.
- Venue
- Proceedings of the 2011 ACM SIGMOD International Conference on Management of data
- Publisher
- ACM
- Published
- 2011-06-12
- Pages
- 193-204
- ISSN
- 0730-8078 (ISSN-L)
- OpenAlex
- W2054922243
- Cited by
- 619
- References
- 2
- FWCI
- 31.91
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Kifer, D., & Machanavajjhala, A. (2011). No free lunch in data privacy. Proceedings of the 2011 ACM SIGMOD International Conference on Management of data. https://doi.org/10.1145/1989323.1989345
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
