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RAPPOR: Randomized Aggregatable Privacy-Preserving Ordinal Response
Open accessAbstract
Randomized Aggregatable Privacy-Preserving Ordinal Response, or RAPPOR, is a technology for crowdsourcing statistics from end-user client software, anonymously, with strong privacy guarantees. In short, RAPPORs allow the forest of client data to be studied, without permitting the possibility of looking at individual trees. By applying randomized response in a novel manner, RAPPOR provides the mechanisms for such collection as well as for efficient, high-utility analysis of the collected data. In particular, RAPPOR permits statistics to be collected on the population of client-side strings with strong privacy guarantees for each client, and without linkability of their reports. This paper describes and motivates RAPPOR, details its differential-privacy and utility guarantees, discusses its practical deployment and properties in the face of different attack models, and, finally, gives results of its application to both synthetic and real-world data.
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“There is some work on adding discrete noise [9], but there is no notion whether such methods are optimal.”
From Advances and Open Problems in Federated Learning (2021) · cites this paper as [9]
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“A number of practical tools, techniques and frameworks have been developed to used for procting data used in ML [79-81, 84, 85] some of which allow a degree of transparency to the applied privacy processes [78].”
From Operationalising ethics in artificial intelligence for healthcare: a framework for AI developers (2023) · cites this paper as [79]
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“Detection methods exploit activation statistics or model properties to determine whether a model is backdoored [151], [152], or whether a training/test example is a backdoor example [47].”
From Privacy and Robustness in Federated Learning: Attacks and Defenses (2024) · cites this paper as [151]
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Distributed Differentially Private Algorithms for Matrix and Tensor Factorization
Imtiaz, H., & Sarwate, A. D. (2018) · IEEE Journal of Selected Topics in Signal Processing · doi:10.1109/jstsp.2018.2877842 · Cited by 32
Weggenmann, B., & Kerschbaum, F. (2018) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3209978.3210008 · Cited by 35
A Lightweight Matrix Factorization for Recommendation With Local Differential Privacy in Big Data
Zhou, H., Yang, G., Xiang, Y., Bai, Y., & Wang, W. (2021) · IEEE Transactions on Big Data · doi:10.1109/tbdata.2021.3139125 · Cited by 35
Metadata
- Authors
- Erlingsson, Ú., Pihur, V., & Korolova, A.
- Affiliations
- Google (United States) (US)
- University of Southern California (US)
- ORCID
- Korolova, Aleksandra
- Venue
- Proceedings of the 2014 ACM SIGSAC Conference on Computer and Communications Security
- Publisher
- ACM
- Published
- 2014-11-03
- Pages
- 1054-1067
- ISSN
- 1543-7221 (ISSN-L)
- OpenAlex
- W1981029888
- Cited by
- 1,838
- References
- 26
- Citations by year
- 2026: 107 · 2025: 186 · 2024: 254 · 2023: 200 · 2022: 192 · 2021: 238 · 2020: 246 · 2019: 168 · 2018: 120 · 2017: 67 · 2016: 45 · 2015: 14 · 2014: 1
- FWCI
- 24.92
- Type
- conference paper
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- ACM SIGSAC Conference on Computer and Communications Security (CCS) (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Physical Sciences › Computer Science › Artificial Intelligence › Internet Traffic Analysis and Secure E-voting
- Social Sciences › Sociology and Political Science › Privacy, Security, and Data Protection
- Keywords
- randomized response, differential privacy, privacy-preserving data collection, linkability, attack models
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