RAPPOR: Randomized Aggregatable Privacy-Preserving Ordinal Response. 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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  1. Farokhi, Farhad: Distributionally-robust machine learning using locally differentially-private data (2022)
  2. Wang, Puyu; Lei, Yunwen; Ying, Yiming; Zhang, Hai: Differentially private SGD with non-smooth losses (2022)
  3. Berrett, Thomas B.; Györfi, László; Walk, Harro: Strongly universally consistent nonparametric regression and classification with privatised data (2021)
  4. Biswas, Sayan; Jung, Kangsoo; Palamidessi, Catuscia: An incentive mechanism for trading personal data in data markets (2021)
  5. Cai, T. Tony; Wang, Yichen; Zhang, Linjun: The cost of privacy: optimal rates of convergence for parameter estimation with differential privacy (2021)
  6. Cheng, Lu; Varshney, Kush R.; Liu, Huan: Socially responsible AI algorithms: issues, purposes, and challenges (2021)
  7. Cunha, Mariana; Mendes, Ricardo; Vilela, João P.: A survey of privacy-preserving mechanisms for heterogeneous data types (2021)
  8. Fioretto, Ferdinando; Van Hentenryck, Pascal; Zhu, Keyu: Differential privacy of hierarchical Census data: an optimization approach (2021)
  9. Ouyang, Jia; Xiao, Yinyin; Liu, Shaopeng; Xiao, Zhenghong; Liao, Xiuxiu: Set-valued data collection with local differential privacy based on category hierarchy (2021)
  10. Pastore, Adriano; Gastpar, Michael: Locally differentially-private randomized response for discrete distribution learning (2021)
  11. Wang, Di; Xu, Jinhui: Inferring ground truth from crowdsourced data under local attribute differential privacy (2021)
  12. Wang, Di; Xu, Jinhui: Differentially private high dimensional sparse covariance matrix estimation (2021)
  13. Ayed, Fadhel; Battiston, Marco; Camerlenghi, Federico: An information theoretic approach to post randomization methods under differential privacy (2020)
  14. Bassily, Raef; Nissim, Kobbi; Stemmer, Uri; Thakurta, Abhradeep: Practical locally private heavy hitters (2020)
  15. Bowen, Claire Mckay; Liu, Fang: Comparative study of differentially private data synthesis methods (2020)
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  17. Hoshino, Nobuaki: A firm foundation for statistical disclosure control (2020)
  18. Luo, Guixun; Zhang, Zhiyuan; Zhang, Zhenjiang; Liu, Yun; Wang, Lifu: A smart privacy-preserving learning method by fake gradients to protect users items in recommender systems (2020)
  19. Luo, Yuan; Jennings, Nicholas R.: A differential privacy mechanism that accounts for network effects for crowdsourcing systems (2020)
  20. Wang, Di; Gaboardi, Marco; Smith, Adam; Xu, Jinhui: Empirical risk minimization in the non-interactive local model of differential privacy (2020)

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