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, but there is no notion whether such methods are optimal.
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From Advances and Open Problems in Federated Learning (2021) · cites this paper as [9]
A number of practical tools, techniques and frameworks have been developed for protecting data used in ML, some of which allow a degree of transparency to the applied privacy processes.
“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]
Detection methods exploit activation statistics or model properties to determine whether a model is backdoored or whether a training/test example is a backdoor example.
“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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Towards Practical Differentially Private Convex Optimization
Iyengar, R., Near, J. P., Song, D. X., Thakkar, O. D., Thakurta, A., & Wang, L. (2019) · IEEE Symposium on Security and Privacy (S&P) · doi:10.1109/sp.2019.00001 · Cited by 134 · see citing works
Age-Dependent Differential Privacy
Zhang, M., Wei, E., Berry, R., & Huang, J. (2023) · IEEE Transactions on Information Theory · doi:10.1109/tit.2023.3340147 · Cited by 134 · see citing works
Locally Differentially Private Heavy Hitter Identification
Wang, T., Li, N., & Jha, S. (2019) · IEEE Transactions on Dependable and Secure Computing · doi:10.1109/tdsc.2019.2927695 · Cited by 129 · see citing works
Marginal Release Under Local Differential Privacy
Cormode, G., Kulkarni, T., & Srivastava, D. (2018) · ACM SIGMOD International Conference on Management of Data (SIGMOD) · doi:10.1145/3183713.3196906 · Cited by 130 · see citing works
Differential Privacy: From Theory to Practice
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Federated Latent Dirichlet Allocation: A Local Differential Privacy Based Framework
Wang, Y., Tong, Y., & Shi, D. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v34i04.6096 · Cited by 122 · see citing works
Privacy- and Utility-Preserving Textual Analysis via Calibrated Multivariate Perturbations
Feyisetan, O., Balle, B., Drake, T. M., & Diethe, T. (2020) · ACM International Conference on Web Search and Data Mining (WSDM) · doi:10.1145/3336191.3371856 · Cited by 126 · see citing works
Context-Aware Generative Adversarial Privacy
Huang, C., Kairouz, P., Chen, X., Sankar, L., & Rajagopal, R. (2017) · Entropy · doi:10.3390/e19120656 · Cited by 156 · see citing works
Using Randomized Response for Differential Privacy Preserving Data Collection
Wang, Y., Wu, X., & Hu, D. (2016) · Cited by 114 · see citing works
ELSA: Secure Aggregation for Federated Learning with Malicious Actors
Rathee, M., Shen, C., Wagh, S., & Popa, R. A. (2023) · IEEE Symposium on Security and Privacy (S&P) · doi:10.1109/sp46215.2023.10179468 · Cited by 117 · see citing works
A differential privacy framework for matrix factorization recommender systems
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Kotsogiannis, I., Tao, Y., He, X., Fanaeepour, M., Machanavajjhala, A., Hay, M., & Miklau, G. (2019) · Proceedings of the VLDB Endowment · doi:10.14778/3342263.3342274 · Cited by 113 · see citing works
Manipulation Attacks in Local Differential Privacy
Cheu, A., Smith, A., & Ullman, J. (2021) · IEEE Symposium on Security and Privacy (S&P) · doi:10.1109/sp40001.2021.00001 · Cited by 119 · see citing works
PFLF: Privacy-Preserving Federated Learning Framework for Edge Computing
Zhou, H., Yang, G., Dai, H., & Liu, G. (2022) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2022.3174394 · Cited by 112 · see citing works
Post-Quantum Era Privacy Protection for Intelligent Infrastructures
Malina, L., Dzurenda, P., Ricci, S., Hajny, J., Srivastava, G., Matulevicius, R., Affia, A.-A. O., Laurent, M., Sultan, N. H., & Tang, Q. (2021) · IEEE Access · doi:10.1109/access.2021.3062201 · Cited by 111 · see citing works
Privacy-Preserving Artificial Intelligence Techniques in Biomedicine
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Billion-scale federated learning on mobile clients
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LF-GDPR: A Framework for Estimating Graph Metrics With Local Differential Privacy
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Wu, G., Wang, S., Ning, Z., & Zhu, B. (2021) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2021.3123643 · Cited by 111 · see citing works
Analyzing Subgraph Statistics from Extended Local Views with Decentralized Differential Privacy
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Answering range queries under local differential privacy
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Desfontaines, D., & Pejó, B. (2020) · Proceedings on Privacy Enhancing Technologies · doi:10.2478/popets-2020-0028 · Cited by 7 · see citing works
Privacy in Deep Learning: A Survey
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Privacy Enhancing Technologies in the Internet of Things: Perspectives and Challenges
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Answering Multi-Dimensional Analytical Queries under Local Differential Privacy
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Is Interaction Necessary for Distributed Private Learning?
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Deep learning methods for fingerprint-based indoor positioning: a review
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A Comprehensive Survey on Local Differential Privacy
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A Review of Privacy-Preserving Federated Learning for the Internet-of-Things
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- 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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