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RAPPOR: Randomized Aggregatable Privacy-Preserving Ordinal Response

Authors:

Úlfar Erlingsson,

Aleksandra KorolovaAuthors Info & Claims

CCS '14: Proceedings of the 2014 ACM SIGSAC Conference on Computer and Communications Security

Pages 1054 - 1067

https://doi.org/10.1145/2660267.2660348

Published: 03 November 2014 Publication History

Abstract

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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Cited By

Li YXu LZhang JZhang L(2025)WF-LDPSR: A local differential privacy mechanism based on water-filling for secure release of trajectory statistics dataComputers & Security10.1016/j.cose.2024.104165148(104165)Online publication date: Jan-2025
https://doi.org/10.1016/j.cose.2024.104165
Hosen MBillah MUddin R(2024)Efficient Privacy-Preserving Scheme for Distributed Machine Learning Against Collusion Attacks (EPPS-DMLCA)European Journal of Theoretical and Applied Sciences10.59324/ejtas.2024.2(5).642:5(714-727)Online publication date: 1-Sep-2024
https://doi.org/10.59324/ejtas.2024.2(5).64
Jefferson Singh David V V. Sumalatha (2024)Effective Data Management using Iterative Approach in Data SystemsInternational Journal of Advanced Research in Science, Communication and Technology10.48175/IJETIR-1248(268-275)Online publication date: 10-Jul-2024
https://doi.org/10.48175/IJETIR-1248
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Index Terms

RAPPOR: Randomized Aggregatable Privacy-Preserving Ordinal Response
1. Mathematics of computing
  1. Information theory
    1. Coding theory
2. Security and privacy
  1. Cryptography
    1. Mathematical foundations of cryptography

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Published In

cover image ACM Conferences

CCS '14: Proceedings of the 2014 ACM SIGSAC Conference on Computer and Communications Security

November 2014

1592 pages

ISBN:9781450329576

DOI:10.1145/2660267

General Chair:
Gail-Joon Ahn
Arizona State University, USA
,
Program Chairs:
Moti Yung
Google -- Columbia University, USA
,
Ninghui Li
Purdue University, USA

Copyright © 2014 Owner/Author.

Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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SIGSAC: ACM Special Interest Group on Security, Audit, and Control

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 03 November 2014

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CCS'14

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SIGSAC

CCS'14: 2014 ACM SIGSAC Conference on Computer and Communications Security

November 3 - 7, 2014

Arizona, Scottsdale, USA

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CCS '14 Paper Acceptance Rate 114 of 585 submissions, 19%;

Overall Acceptance Rate 1,261 of 6,999 submissions, 18%

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CCS '25

Sponsor:
sigsac

ACM SIGSAC Conference on Computer and Communications Security

October 13 - 17, 2025

Taipei , Taiwan

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Cited By

Li YXu LZhang JZhang L(2025)WF-LDPSR: A local differential privacy mechanism based on water-filling for secure release of trajectory statistics dataComputers & Security10.1016/j.cose.2024.104165148(104165)Online publication date: Jan-2025
https://doi.org/10.1016/j.cose.2024.104165
Hosen MBillah MUddin R(2024)Efficient Privacy-Preserving Scheme for Distributed Machine Learning Against Collusion Attacks (EPPS-DMLCA)European Journal of Theoretical and Applied Sciences10.59324/ejtas.2024.2(5).642:5(714-727)Online publication date: 1-Sep-2024
https://doi.org/10.59324/ejtas.2024.2(5).64
Jefferson Singh David V V. Sumalatha (2024)Effective Data Management using Iterative Approach in Data SystemsInternational Journal of Advanced Research in Science, Communication and Technology10.48175/IJETIR-1248(268-275)Online publication date: 10-Jul-2024
https://doi.org/10.48175/IJETIR-1248
Kaur SSingh G(2024)Adoption of Artificial Intelligence in MarketingNeurosensory and Neuromarketing Impacts on Consumer Behavior10.4018/979-8-3693-8222-6.ch001(1-32)Online publication date: 4-Oct-2024
https://doi.org/10.4018/979-8-3693-8222-6.ch001
Alauthman MAl-Qerem AAlmomani AAldweesh AAburub FAlkasassbeh M(2024)Privacy-Preserving Machine Learning Cryptographic Techniques for Secure Data AnalysisInnovations in Modern Cryptography10.4018/979-8-3693-5330-1.ch017(405-430)Online publication date: 12-Jul-2024
https://doi.org/10.4018/979-8-3693-5330-1.ch017
Yao ZTan LYi JFu LZhang ZTan XXie JShe KYang PWu WYe DYu Z(2024)Sensitive Data Privacy Protection of Carrier in Intelligent Logistics SystemSymmetry10.3390/sym1601006816:1(68)Online publication date: 4-Jan-2024
https://doi.org/10.3390/sym16010068
Li KZhang HXu YLiu Z(2024)A Range Query Scheme for Spatial Data with Shuffled Differential PrivacyMathematics10.3390/math1213193412:13(1934)Online publication date: 21-Jun-2024
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Feretzakis GPapaspyridis KGkoulalas-Divanis AVerykios V(2024)Privacy-Preserving Techniques in Generative AI and Large Language Models: A Narrative ReviewInformation10.3390/info1511069715:11(697)Online publication date: 4-Nov-2024
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Jiang YRezazadeh Baee MSimpson LGauravaram PPieprzyk JZia TZhao ZLe Z(2024)Pervasive User Data Collection from Cyberspace: Privacy Concerns and CountermeasuresCryptography10.3390/cryptography80100058:1(5)Online publication date: 31-Jan-2024
https://doi.org/10.3390/cryptography8010005
Horigome HKikuchi HFujita MYu C(2024)Robust Estimation Method against Poisoning Attacks for Key-Value Data with Local Differential PrivacyApplied Sciences10.3390/app1414636814:14(6368)Online publication date: 22-Jul-2024
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