research-article

Detecting privacy leaks using corpus-based association rules

Authors:

Philippe Golle,

Jessica StaddonAuthors Info & Claims

KDD '08: Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining

Pages 893 - 901

https://doi.org/10.1145/1401890.1401997

Published: 24 August 2008 Publication History

Abstract

Detecting inferences in documents is critical for ensuring privacy when sharing information. In this paper, we propose a refined and practical model of inference detection using a reference corpus. Our model is inspired by association rule mining: inferences are based on word co-occurrences. Using the model and taking the Web as the reference corpus, we can find inferences and measure their strength through web-mining algorithms that leverage search engines such as Google or Yahoo!.

Our model also includes the important case of private corpora, to model inference detection in enterprise settings in which there is a large private document repository. We find inferences in private corpora by using analogues of our Web-mining algorithms, relying on an index for the corpus rather than a Web search engine.

We present results from two experiments. The first experiment demonstrates the performance of our techniques in identifying all the keywords that allow for inference of a particular topic (e.g. "HIV") with confidence above a certain threshold. The second experiment uses the public Enron e-mail dataset. We postulate a sensitive topic and use the Enron corpus and the Web together to find inferences for the topic.

These experiments demonstrate that our techniques are practical, and that our model of inference based on word co-occurrence is well-suited to efficient inference detection.

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Detecting privacy leaks using corpus-based association rules

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Information & Contributors

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Published In

cover image ACM Conferences

KDD '08: Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining

August 2008

1116 pages

ISBN:9781605581934

DOI:10.1145/1401890

General Chair:
Ying Li
Microsoft adCenter Labs
,
Program Chairs:
Bing Liu
University of Illinois at Chicago
,
Sunita Sarawagi
Indian Institute of Technology, Bombay

Copyright © 2008 ACM.

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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Published: 24 August 2008

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KDD08: The 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

August 24 - 27, 2008

Nevada, Las Vegas, USA

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KDD '08 Paper Acceptance Rate 118 of 593 submissions, 20%;

Overall Acceptance Rate 1,133 of 8,635 submissions, 13%

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Cited By

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https://dl.acm.org/doi/10.1145/3603719.3603734
Gambarelli GGangemi ATripodi R(2023)Is Your Model Sensitive? SPEDAC: A New Resource for the Automatic Classification of Sensitive Personal DataIEEE Access10.1109/ACCESS.2023.324008911(10864-10880)Online publication date: 2023
https://doi.org/10.1109/ACCESS.2023.3240089
AlShuhail AShao J(2023)Semantic Attack on Disassociated Transaction DataSN Computer Science10.1007/s42979-023-01781-64:4Online publication date: 20-Apr-2023
https://dl.acm.org/doi/10.1007/s42979-023-01781-6
Gambarelli GGangemi A(2022)PRIVAFRAME: A Frame-Based Knowledge Graph for Sensitive Personal DataBig Data and Cognitive Computing10.3390/bdcc60300906:3(90)Online publication date: 26-Aug-2022
https://doi.org/10.3390/bdcc6030090
Pilán ILison PØvrelid LPapadopoulou ASánchez DBatet M(2022)The Text Anonymization Benchmark (TAB): A Dedicated Corpus and Evaluation Framework for Text AnonymizationComputational Linguistics10.1162/coli_a_0045848:4(1053-1101)Online publication date: 1-Dec-2022
https://doi.org/10.1162/coli_a_00458
Liebowitz DNepal SMoore KChristopher CKanhere SNguyen DTimmer RLongland MRathakumar K(2021)Deception for Cyber Defence: Challenges and Opportunities2021 Third IEEE International Conference on Trust, Privacy and Security in Intelligent Systems and Applications (TPS-ISA)10.1109/TPSISA52974.2021.00020(173-182)Online publication date: Dec-2021
https://doi.org/10.1109/TPSISA52974.2021.00020
Timmer RLiebowitz DNepal SKanhere S(2021)Can pre-trained Transformers be used in detecting complex sensitive sentences? - A Monsanto case study2021 Third IEEE International Conference on Trust, Privacy and Security in Intelligent Systems and Applications (TPS-ISA)10.1109/TPSISA52974.2021.00010(90-97)Online publication date: Dec-2021
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Hassan FSanchez DDomingo-Ferrer J(2021)Utility-Preserving Privacy Protection of Textual Documents via Word EmbeddingsIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2021.3076632(1-1)Online publication date: 2021
https://doi.org/10.1109/TKDE.2021.3076632
Umar PSquicciarini ARajtmajer S(2020)A Study of Self-Privacy Violations in Online Public Discourse2020 IEEE International Conference on Big Data (Big Data)10.1109/BigData50022.2020.9378163(1041-1050)Online publication date: 10-Dec-2020
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Komal Kumar NVigneswari D(2020)A Multi-level Access Technique for Privacy-Preserving Perturbation in Association Rule MiningAdvances in Artificial Intelligence and Data Engineering10.1007/978-981-15-3514-7_48(631-645)Online publication date: 14-Aug-2020
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