The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.
Differential privacy under continual observation
Abstract
Differential privacy is a recent notion of privacy tailored to privacy-preserving data analysis [11]. Up to this point, research on differentially private data analysis has focused on the setting of a trusted curator holding a large, static, data set; thus every computation is a "one-shot" object: there is no point in computing something twice, since the result will be unchanged, up to any randomness introduced for privacy. However, many applications of data analysis involve repeated computations, either because the entire goal is one of monitoring, e.g., of traffic conditions, search trends, or incidence of influenza, or because the goal is some kind of adaptive optimization, e.g., placement of data to minimize access costs. In these cases, the algorithm must permit continual observation of the system's state. We therefore initiate a study of differential privacy under continual observation. We identify the problem of maintaining a counter in a privacy preserving manner and show its wide applicability to many different problems.
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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-citationL -diversity: Privacy beyond k -anonymity
Machanavajjhala, A., Kifer, D., Gehrke, J., & Venkitasubramaniam, M. (2007) · ACM Transactions on Knowledge Discovery from Data · doi:10.1145/1217299.1217302 · Cited by 3,617
t-Closeness: Privacy Beyond k-Anonymity and l-Diversity
Li, N., Li, T., & Venkatasubramanian, S. (2007) · 2007 IEEE 23rd International Conference on Data Engineering · doi:10.1109/icde.2007.367856 · Cited by 3,451
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
Self-citation"You Might Also Like:" Privacy Risks of Collaborative Filtering
Calandrino, J. A., Kilzer, A., Narayanan, A., Felten, E. W., & Shmatikov, V. (2011) · 2011 IEEE Symposium on Security and Privacy · doi:10.1109/sp.2011.40 · Cited by 311
Cited by
The Algorithmic Foundations of Differential Privacy
Dwork, C., & Roth, A. (2013) · doi:10.1561/9781601988195 · Cited by 3,856
Self-citationRAPPOR: 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
Differential Privacy Techniques for Cyber Physical Systems: A Survey
Hassan, M. U., Rehmani, M. H., & Chen, J. (2020) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2019.2944748 · Cited by 564
"You Might Also Like:" Privacy Risks of Collaborative Filtering
Calandrino, J. A., Kilzer, A., Narayanan, A., Felten, E. W., & Shmatikov, V. (2011) · 2011 IEEE Symposium on Security and Privacy · doi:10.1109/sp.2011.40 · Cited by 311
Mobile sensor data anonymization
Malekzadeh, M., Clegg, R. G., Cavallaro, A., & Haddadi, H. (2019) · Proceedings of the International Conference on Internet of Things Design and Implementation · doi:10.1145/3302505.3310068 · Cited by 298
Differentially Private Distributed Optimization
Huang, Z., Mitra, S., & Vaidya, N. (2015) · Proceedings of the 16th International Conference on Distributed Computing and Networking · doi:10.1145/2684464.2684480 · Cited by 266
Amplification by Shuffling: From Local to Central Differential Privacy via Anonymity
Erlingsson, Ú., Feldman, V., Mironov, I., Raghunathan, A., Talwar, K., & Thakurta, A. (2019) · Proceedings of the Thirtieth Annual ACM-SIAM Symposium on Discrete Algorithms · doi:10.1137/1.9781611975482.151 · Cited by 264
A Berkeley View of Systems Challenges for AI
Stoica, I., Song, D., Popa, R. A., Patterson, D., Mahoney, M. W., Katz, R., Joseph, A. D., Jordan, M., Hellerstein, J. M., Gonzalez, J. E., et al. (2017) · arXiv · doi:10.48550/arxiv.1712.05855 · Cited by 171
Privacy in Internet of Things: From Principles to Technologies
Li, C., & Palanisamy, B. (2019) · IEEE Internet of Things Journal · doi:10.1109/jiot.2018.2864168 · Cited by 130
Practical and private (deep) learning without sampling or shuffling
Kairouz, P., McMahan, B., Song, S., Thakkar, O., Thakurta, A., & Xu, Z. (2021) · arXiv · doi:10.48550/arxiv.2103.00039 · Cited by 52
Global and Local Differential Privacy for Collaborative Bandits
Wang, H., Zhao, Q., Wu, Q., Chopra, S., Khaitan, A., & Wang, H. (2020) · Fourteenth ACM Conference on Recommender Systems · doi:10.1145/3383313.3412254 · Cited by 28
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
Metadata
- Authors
- Dwork, C., Naor, M., Pitassi, T., & Rothblum, G. N.
- Venue
- Proceedings of the forty-second ACM symposium on Theory of computing
- Publisher
- ACM
- Published
- 2010-06-05
- Pages
- 715-724
- OpenAlex
- W2009611335
- Cited by
- 583
- References
- 5
- FWCI
- 14.55
- Type
- conference paper
- Language
- en
- Open access
- closed
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