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Private and Continual Release of Statistics

Published: 01 November 2011 Publication History

Abstract

We ask the question: how can Web sites and data aggregators continually release updated statistics, and meanwhile preserve each individual user’s privacy? Suppose we are given a stream of 0’s and 1’s. We propose a differentially private continual counter that outputs at every time step the approximate number of 1’s seen thus far. Our counter construction has error that is only poly-log in the number of time steps. We can extend the basic counter construction to allow Web sites to continually give top-k and hot items suggestions while preserving users’ privacy.

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    Published In

    cover image ACM Transactions on Information and System Security
    ACM Transactions on Information and System Security  Volume 14, Issue 3
    November 2011
    133 pages
    ISSN:1094-9224
    EISSN:1557-7406
    DOI:10.1145/2043621
    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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    Publication History

    Published: 01 November 2011
    Accepted: 01 June 2011
    Revised: 01 January 2011
    Received: 01 May 2010
    Published in TISSEC Volume 14, Issue 3

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    Author Tags

    1. Differential privacy
    2. continual mechanism
    3. streaming algorithm

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    Cited By

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    • (2024)Enhancing Real-Time Traffic Data Sharing: A Differential Privacy-Based Scheme with Spatial CorrelationMathematics10.3390/math1211172212:11(1722)Online publication date: 31-May-2024
    • (2024)Benchmarking Secure Sampling Protocols for Differential PrivacyProceedings of the 2024 on ACM SIGSAC Conference on Computer and Communications Security10.1145/3658644.3690257(318-332)Online publication date: 2-Dec-2024
    • (2024)Continual Release of Differentially Private Synthetic Data from Longitudinal Data CollectionsProceedings of the ACM on Management of Data10.1145/36515952:2(1-26)Online publication date: 14-May-2024
    • (2024)Online Differentially Private Synthetic Data GenerationIEEE Transactions on Privacy10.1109/TP.2024.34866871(19-30)Online publication date: 2024
    • (2024)Secure and Efficient Continuous Learning Model for Traffic Flow PredictionIEEE Transactions on Network and Service Management10.1109/TNSM.2024.340795921:4(4900-4911)Online publication date: 31-May-2024
    • (2024)Crowdsensing Data Trading for Unknown Market: Privacy, Stability, and ConflictsIEEE Transactions on Mobile Computing10.1109/TMC.2024.339981623:12(11719-11734)Online publication date: Dec-2024
    • (2024)Age-Dependent Differential PrivacyIEEE Transactions on Information Theory10.1109/TIT.2023.334014770:2(1300-1319)Online publication date: 1-Feb-2024
    • (2024)Boosting Accuracy of Differentially Private Continuous Data Release for Federated LearningIEEE Transactions on Information Forensics and Security10.1109/TIFS.2024.347732519(10287-10301)Online publication date: 2024
    • (2024)Distributed Differential Privacy via Shuffling Versus Aggregation: A Curious StudyIEEE Transactions on Information Forensics and Security10.1109/TIFS.2024.335147419(2501-2516)Online publication date: 1-Jan-2024
    • (2024)A Federated Learning Framework Based on Differentially Private Continuous Data ReleaseIEEE Transactions on Dependable and Secure Computing10.1109/TDSC.2024.336406021:5(4879-4894)Online publication date: 1-Sep-2024
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