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On sampling, anonymization, and differential privacy or, k-anonymization meets differential privacy

Authors:

Ninghui Li,

Wahbeh Qardaji,

Dong SuAuthors Info & Claims

ASIACCS '12: Proceedings of the 7th ACM Symposium on Information, Computer and Communications Security

Pages 32 - 33

https://doi.org/10.1145/2414456.2414474

Published: 02 May 2012 Publication History

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Abstract

This paper aims at answering the following two questions in privacy-preserving data analysis and publishing. The first is: What formal privacy guarantee (if any) does k-anonymization methods provide? k-Anonymization methods have been studied extensively in the database community, but have been known to lack strong privacy guarantees. The second question is: How can we benefit from the adversary's uncertainty about the data? More specifically, can we come up a meaningful relaxation of differential privacy [2, 3] by exploiting the adversary's uncertainty about the dataset? We now discuss these two motivations in more detail.

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Cited By

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Waseda ANojima RWang L(2024)A Differentially Private (Random) Decision Tree without Noise from k-AnonymityApplied Sciences10.3390/app1417762514:17(7625)Online publication date: 28-Aug-2024
https://doi.org/10.3390/app14177625
Wang YZhou TChen CWang Y(2024)Federated Submodular Maximization With Differential PrivacyIEEE Internet of Things Journal10.1109/JIOT.2023.332480111:2(1827-1839)Online publication date: 15-Jan-2024
https://doi.org/10.1109/JIOT.2023.3324801
Yamamoto AShibuya T(2024)Privacy-Optimized Randomized Response for Sharing Multi-Attribute Data2024 IEEE Symposium on Computers and Communications (ISCC)10.1109/ISCC61673.2024.10733730(1-8)Online publication date: 26-Jun-2024
https://doi.org/10.1109/ISCC61673.2024.10733730
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Index Terms

On sampling, anonymization, and differential privacy or, k-anonymization meets differential privacy
1. Security and privacy
  1. Human and societal aspects of security and privacy
2. Social and professional topics
  1. Computing / technology policy
    1. Computer crime
    2. Privacy policies

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Published In

ASIACCS '12: Proceedings of the 7th ACM Symposium on Information, Computer and Communications Security

May 2012

119 pages

ISBN:9781450316484

DOI:10.1145/2414456

General Chairs:
Heung Youl Youm
SoonChunHyang University, Korea
,
Yoojae Won
Kisa, Korea

Permission to make digital or hard copies of all or part 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 components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

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Publication History

Published: 02 May 2012

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ASIA CCS '12: 7th ACM Symposium on Information, Compuer and Communications Security

May 2 - 4, 2012

Seoul, Korea

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Overall Acceptance Rate 418 of 2,322 submissions, 18%

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Cited By

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Waseda ANojima RWang L(2024)A Differentially Private (Random) Decision Tree without Noise from k-AnonymityApplied Sciences10.3390/app1417762514:17(7625)Online publication date: 28-Aug-2024
https://doi.org/10.3390/app14177625
Wang YZhou TChen CWang Y(2024)Federated Submodular Maximization With Differential PrivacyIEEE Internet of Things Journal10.1109/JIOT.2023.332480111:2(1827-1839)Online publication date: 15-Jan-2024
https://doi.org/10.1109/JIOT.2023.3324801
Yamamoto AShibuya T(2024)Privacy-Optimized Randomized Response for Sharing Multi-Attribute Data2024 IEEE Symposium on Computers and Communications (ISCC)10.1109/ISCC61673.2024.10733730(1-8)Online publication date: 26-Jun-2024
https://doi.org/10.1109/ISCC61673.2024.10733730
Garg STorra V(2024)Privacy in manifolds: Combining k-anonymity with differential privacy on Fréchet meansComputers & Security10.1016/j.cose.2024.103983144(103983)Online publication date: Sep-2024
https://doi.org/10.1016/j.cose.2024.103983
Lawrance JJesudhasan JThampiraj Rittammal J(2024)Parallel Fuzzy C-Means Clustering Based Big Data Anonymization Using Hadoop MapReduceWireless Personal Communications10.1007/s11277-024-11101-7135:4(2103-2130)Online publication date: 14-May-2024
https://doi.org/10.1007/s11277-024-11101-7
Sugiyama TOosugi HYamanaka IMinami K(2024)Utility Analysis of Differentially Private Anonymized Data Based on Random SamplingPrivacy in Statistical Databases10.1007/978-3-031-69651-0_3(35-47)Online publication date: 25-Sep-2024
https://dl.acm.org/doi/10.1007/978-3-031-69651-0_3
Lai PAriel Pinto C(2024)Privacy in Federated Learning Natural Language ModelsHandbook of Trustworthy Federated Learning10.1007/978-3-031-58923-2_9(259-287)Online publication date: 10-May-2024
https://doi.org/10.1007/978-3-031-58923-2_9
Chen WSong DÖzgür AKairouz POh ANaumann TGloberson ASaenko KHardt MLevine S(2023)Privacy amplification via compressionProceedings of the 37th International Conference on Neural Information Processing Systems10.5555/3666122.3669152(69202-69227)Online publication date: 10-Dec-2023
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Zhang WZhang RKrause ABrunskill ECho KEngelhardt BSabato SScarlett J(2023)DP-fast MHProceedings of the 40th International Conference on Machine Learning10.5555/3618408.3620166(41847-41860)Online publication date: 23-Jul-2023
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Rust PSøgaard AKrause ABrunskill ECho KEngelhardt BSabato SScarlett J(2023)Differential privacy, linguistic fairness, and training data influenceProceedings of the 40th International Conference on Machine Learning10.5555/3618408.3619629(29354-29387)Online publication date: 23-Jul-2023
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