The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Building a RAPPOR with the Unknown: Privacy-Preserving Learning of Associations and Data Dictionaries
Abstract
Techniques based on randomized response enable the collection of potentially sensitive data from clients in a privacy-preserving manner with strong local differential privacy guarantees. A recent such technology, RAPPOR [12], enables estimation of the marginal frequencies of a set of strings via privacy-preserving crowdsourcing. However, this original estimation process relies on a known dictionary of possible strings; in practice, this dictionary can be extremely large and/or unknown. In this paper, we propose a novel decoding algorithm for the RAPPOR mechanism that enables the estimation of “unknown unknowns,” i.e., strings we do not know we should be estimating. To enable learning without explicit dictionary knowledge, we develop methodology for estimating the joint distribution of multiple variables collected with RAPPOR. Our contributions are not RAPPOR-specific, and can be generalized to other local differential privacy mechanisms for learning distributions of string-valued random variables.
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Cites
Agresti, A. (2002) · Wiley Series in Probability and Statistics · doi:10.1002/0471249688 · Cited by 6,746
Randomized Response: A Survey Technique for Eliminating Evasive Answer Bias
Warner, S. L. (1965) · Journal of the American Statistical Association · doi:10.2307/2283137 · Cited by 3,263
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
Self-citation
Cited by
Prochlo: Strong Privacy for Analytics in the Crowd
Bittau, A., Erlingsson, Ú., Maniatis, P., Mironov, I., Raghunathan, A., Lie, D., Rudominer, M., Kode, U., Tinnes, J., & Seefeld, B. (2017) · Proceedings of the 26th Symposium on Operating Systems Principles · doi:10.1145/3132747.3132769 · Cited by 378
“[30], such multidimensional analysis over long-tail distributions (of Web pages) is a poor fit for local differential privacy methods.”
BackgroundSelf-citationAmplification 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
Self-citationTrusted AI in Multiagent Systems: An Overview of Privacy and Security for Distributed Learning
Ma, C., Li, J., Wei, K., Liu, B., Ding, M., Yuan, L., Han, Z., & Vincent Poor, H. (2023) · Proceedings of the IEEE · doi:10.1109/jproc.2023.3306773 · Cited by 96
A survey of local differential privacy for securing internet of vehicles
Zhao, P., Zhang, G., Wan, S., Liu, G., & Umer, T. (2020) · The Journal of Supercomputing · doi:10.1007/s11227-019-03104-0 · Cited by 66
Privacy-preserving aggregation of personal health data streams
Kim, J. W., Jang, B., & Yoo, H. (2018) · PLOS ONE · doi:10.1371/journal.pone.0207639 · Cited by 49
Metadata
- Authors
- Fanti, G., Pihur, V., & Erlingsson, Ú.
- Venue
- Proceedings on Privacy Enhancing Technologies
- Published
- 2016-05-06
- Volume
- 2016
- Issue
- 3
- Pages
- 41-61
- ISSN
- 2299-0984
- OpenAlex
- W2964225135
- Cited by
- 275
- References
- 3
- FWCI
- 26.56
- Type
- journal article
- Language
- en
- Open access
- closed
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