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Privacy-Preserving Publishing of Multilevel Utility-Controlled Graph Datasets

Published: 22 February 2018 Publication History

Abstract

Conventional private data publication schemes are targeted at publication of sensitive datasets either after the k-anonymization process or through differential privacy constraints. Typically these schemes are designed with the objective of retaining as much utility as possible for the aggregate queries while ensuring the privacy of the individual records. Such an approach, though suitable for publishing aggregate information as public datasets, is inapplicable when users have different levels of access to the same data. We argue that existing schemes either result in increased disclosure of private information or lead to reduced utility when some users have more access privileges than the others. In this article, we present an anonymization framework for publishing large datasets with the goals of providing different levels of utility to the users based on their access privilege levels. We design and implement our proposed multilevel utility-controlled anonymization schemes in the context of large association graphs considering three levels of user utility, namely, (1) users having access to only the graph structure, (2) users having access to the graph structure and aggregate query results, and (3) users having access to the graph structure, aggregate query results, and individual associations. Our experiments on real large association graphs show that the proposed techniques are effective and scalable and yield the required level of privacy and utility for each user privacy and access privilege level.

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    Published In

    cover image ACM Transactions on Internet Technology
    ACM Transactions on Internet Technology  Volume 18, Issue 2
    Special Issue on Internetware and Devops and Regular Papers
    May 2018
    294 pages
    ISSN:1533-5399
    EISSN:1557-6051
    DOI:10.1145/3182619
    • Editor:
    • Munindar P. Singh
    Issue’s Table of Contents
    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: 22 February 2018
    Accepted: 01 July 2017
    Revised: 01 May 2017
    Received: 01 March 2017
    Published in TOIT Volume 18, Issue 2

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    Author Tags

    1. Data privacy
    2. association datasets
    3. bipartite graphs
    4. data anonymization
    5. multilevel privacy

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