The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
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
Self-citation
Cited by
Items 101–118 of 118
Antwi-Boasiako, E., Zhou, S., Liao, Y., Liu, Q., Wang, Y., & Owusu-Agyemang, K. (2021) · Journal of Information Security and Applications · doi:10.1016/j.jisa.2021.102949 · Cited by 31
Protecting Semantic Trajectory Privacy for VANET with Reinforcement Learning
Wang, W., Min, M., Xiao, L., Chen, Y., & Dai, H. (2019) · ICC 2019 - 2019 IEEE International Conference on Communications (ICC) · doi:10.1109/icc.2019.8761415 · Cited by 31
Polat, M., Karataş, İ. H., & Varol, N. (2025) · Leadership and Policy in Schools · doi:10.1080/15700763.2024.2412204 · Cited by 28
AirGapAgent: Protecting Privacy-Conscious Conversational Agents
Bagdasarian, E., Yi, R., Ghalebikesabi, S., Kairouz, P., Gruteser, M., Oh, S., Balle, B., & Ramage, D. (2024) · Proceedings of the 2024 on ACM SIGSAC Conference on Computer and Communications Security · doi:10.1145/3658644.3690350 · Cited by 28
“Other methods that protect data, like local differential privacy (DP) [23, 24, 33, 61], are not context-aware and therefore reduce the agent’s utility by always introducing noise on user data [17, 22, 32].”
On the Power of Multiple Anonymous Messages
Ghazi, B., Golowich, N., Kumar, R., Pagh, R., & Velingker, A. (2019) · arXiv · doi:10.48550/arxiv.1908.11358 · Cited by 26
Integrating Technical and Legal Concepts of Privacy
Sokolovska, A., & Kocarev, L. (2018) · IEEE Access · doi:10.1109/access.2018.2836184 · Cited by 24
A comprehensive survey and taxonomy on privacy-preserving deep learning
Tran, A.-T., Luong, T.-D., & Huynh, V.-N. (2024) · Neurocomputing · doi:10.1016/j.neucom.2024.127345 · Cited by 21
Weimann, K., & Conrad, T. O. F. (2024) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2024.3427787 · Cited by 19
KHyperLogLog: Estimating Reidentifiability and Joinability of Large Data at Scale
Chia, P. H., Desfontaines, D., Perera, I. M., Simmons-Marengo, D., Li, C., Day, W.-Y., Wang, Q., & Guevara, M. (2019) · 2019 IEEE Symposium on Security and Privacy (SP) · doi:10.1109/sp.2019.00046 · Cited by 15
Differential privacy‐guaranteed trajectory community identification over vehicle ad‐hoc networks
Zhou, Z., Qiao, Y., Zhu, L., Guan, J., Liu, Y., & Xu, C. (2018) · Internet Technology Letters · doi:10.1002/itl2.9 · Cited by 11
Segmentations-Leak: Membership Inference Attacks and Defenses in Semantic Image Segmentation
He, Y., Rahimian, S., Schiele, B., & Fritz, M. (2019) · arXiv · doi:10.48550/arxiv.1912.09685 · Cited by 10
Sustainable AI: Emerging Trends, Impacts, and Future Challenges
Philipo, A. G., Ning, H., Sarwatt, D. S., Mohamed, J. A., Yusufu, A. S., Shi, F., Urenje, S., & Ding, J. (2025) · IEEE Transactions on Sustainable Computing · doi:10.1109/tsusc.2025.3611272 · Cited by 9
Understanding the protection of privacy when counting subway travelers through anonymization
Shafaeipour, N., Stanciu, V.-D., van Steen, M., & Wang, M. (2024) · Computers, Environment and Urban Systems · doi:10.1016/j.compenvurbsys.2024.102091 · Cited by 6
Minimax Rates of Estimating Approximate Differential Privacy
Liu, X., & Oh, S. (2019) · arXiv · doi:10.48550/arxiv.1905.10335 · Cited by 4
Automatic Discovery of Privacy–Utility Pareto Fronts
Avent, B., González, J., Diethe, T., Paleyes, A., & Balle, B. (2020) · Proceedings on Privacy Enhancing Technologies · doi:10.2478/popets-2020-0060 · Cited by 3
Why do classifier accuracies show linear trends under distribution shift?
Mania, H., & Sra, S. (2020) · arXiv · doi:10.48550/arxiv.2012.15483 · Cited by 3
Directions of Technical Innovation for Regulatable AI Systems
Shen, X., Brown, H., Tao, J., Strobel, M., Tong, Y., Narayan, A., Soh, H., & Doshi-Velez, F. (2024) · Communications of the ACM · doi:10.1145/3653670 · Cited by 3
Segar, N., & Vijayan, V. (2026) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2026.1804943
“Differential privacy has emerged as the gold standard for formal privacy guarantees, ensuring that the presence or absence of any individual’s data has bounded influence on query outputs (Dwork, 2006; Dwork and Roth, 2014).”
Metadata
- Authors
- Dwork, C.
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer Berlin Heidelberg
- Published
- 2006
- Pages
- 1-12
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W2911978475
- Cited by
- 5,291
- References
- 1
- FWCI
- 20.17
- Type
- conference paper
- Language
- en
- Open access
- closed
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
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.
