• Login
    View Item 
    •   Home
    • Research
    • Articles
    • View Item
    •   Home
    • Research
    • Articles
    • View Item
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    Browse

    All of KAUSTCommunitiesIssue DateSubmit DateThis CollectionIssue DateSubmit Date

    My Account

    Login

    Quick Links

    Open Access PolicyORCID LibguidePlumX LibguideSubmit an Item

    Statistics

    Display statistics

    Inferring ground truth from crowdsourced data under local attribute differential privacy

    • CSV
    • RefMan
    • EndNote
    • BibTex
    • RefWorks
    Thumbnail
    Name:
    TCS2021_truth.pdf
    Size:
    499.1Kb
    Format:
    PDF
    Description:
    Accepted manuscript
    Embargo End Date:
    2023-02-01
    Download
    Type
    Article
    Authors
    Wang, Di
    Xu, Jinhui
    KAUST Department
    King Abdullah University of Science and Technology, Thuwal, Saudi Arabia
    Date
    2021-02
    Embargo End Date
    2023-02-01
    Submitted Date
    2020-06-21
    Permanent link to this record
    http://hdl.handle.net/10754/668012
    
    Metadata
    Show full item record
    Abstract
    Nowadays, crowdsourcing gains an increasing popularity as it can be adopted to solve many challenging question answering tasks that are easy for humans but difficult for computers. Due to the variety in the quality of users, it is important to infer not only the underlying ground truth of these tasks but also the users ability from the answers given by users. This problem is called Ground Truth Inference and has been studied for many years. However, since the answers collected from the users may contain sensitive information, ground truth inference raises serious privacy concern. Due to this reason, the problem of ground truth inference under local differential privacy (LDP) model has been recently studied. However, this problem is still not well understood and even some basic questions have not been solved yet. First, it is still unknown what is the average error of the private estimators to the underlying ground truth. Secondly, we do not know whether we can infer the ability of each user under LDP model and what is the estimation error w.r.t. the underlying users ability. Finally, previous work only shows that their methods have better performance than the private major voting algorithm through experiments. However, there is still no theoretically result which shows this priority formally or mathematically. In this paper, we partially solve these problems by studying the ground truth inference problem under local attribute differential privacy (LADP) model, which is a relaxation of LDP model, and propose a new algorithm called private Dawid-Skene method, which is motivated by the classical Dawid-Skene method. Specifically, we first provide the estimation errors for both ability of users and the ground truth under some assumptions of the problem if the algorithm start with some appropriate initial vector. Moreover, we propose an explicit instance and show that the estimation error of the ground truth achieved by the private major voting algorithm is always greater than the error achieved by our method.
    Citation
    Wang, D., & Xu, J. (2021). Inferring ground truth from crowdsourced data under local attribute differential privacy. Theoretical Computer Science. doi:10.1016/j.tcs.2021.02.039
    Sponsors
    This research was supported in part by the baseline funding of KAUST, the National Science Foundation (NSF) through grants CCF-1422324 and CCF-1716400.
    Publisher
    Elsevier BV
    Journal
    Theoretical Computer Science
    DOI
    10.1016/j.tcs.2021.02.039
    Additional Links
    https://linkinghub.elsevier.com/retrieve/pii/S0304397521001237
    ae974a485f413a2113503eed53cd6c53
    10.1016/j.tcs.2021.02.039
    Scopus Count
    Collections
    Articles

    entitlement

     
    DSpace software copyright © 2002-2021  DuraSpace
    Quick Guide | Contact Us | KAUST University Library
    Open Repository is a service hosted by 
    Atmire NV
     

    Export search results

    The export option will allow you to export the current search results of the entered query to a file. Different formats are available for download. To export the items, click on the button corresponding with the preferred download format.

    By default, clicking on the export buttons will result in a download of the allowed maximum amount of items. For anonymous users the allowed maximum amount is 50 search results.

    To select a subset of the search results, click "Selective Export" button and make a selection of the items you want to export. The amount of items that can be exported at once is similarly restricted as the full export.

    After making a selection, click one of the export format buttons. The amount of items that will be exported is indicated in the bubble next to export format.