{"title":"Privacy-preserving quadratic truth discovery based on Precision partitioning","authors":"","doi":"10.1016/j.cose.2024.104039","DOIUrl":null,"url":null,"abstract":"<div><p>Truth discovery technology is widely used in the field of data collection in crowdsourcing; however, with the deepening of people’s privacy awareness, ordinary truth discovery can no longer meet the current user demand for privacy protection, and solving the privacy problem is one of the critical challenges of truth discovery. A number of works have been proposed as the truth discovery mechanism of differential privacy. However, the privacy budget allocation in the known differential privacy truth discovery mechanisms does not consider the data precision differences of multi-source datasets and the anomalous results of small-value data after aggregation. We propose a precision division based on a differential privacy two-layer truth discovery framework to ensure privacy security while considering data precision and can also ensure high-accuracy truth discovery. The main challenges of this paper are how to obtain highly accurate truth values in sparse data scenarios without exposing the original values to the cloud server when performing error correction of aggregated anomalies after noise addition, as well as to improve the precision of truth value estimation of perturbed streaming data based on the refinement of the privacy protection level based on the accuracy of the data. Specifically, we first formulate a data-sampling algorithm to get the data precision of different users and to sieve out the anomalous data and duplicate data to obtain the quality of user-uploaded data. Then, we formulate a new privacy budget allocation mechanism, which synthesizes the sampling situation during data preprocessing and fully considers the data precision to quantify user privacy and turn it into specific values. We provide a quadratic truth discovery mechanism based on a predictive interpolation algorithm when dealing with small-value data, ensuring the reliability of small data aggregation results. We demonstrate that our framework achieves differential privacy for user-supplied data while we conduct extensive experiments on three real-world datasets to prove the effectiveness of our system framework.</p></div>","PeriodicalId":51004,"journal":{"name":"Computers & Security","volume":null,"pages":null},"PeriodicalIF":4.8000,"publicationDate":"2024-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computers & Security","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0167404824003444","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
引用次数: 0
Abstract
Truth discovery technology is widely used in the field of data collection in crowdsourcing; however, with the deepening of people’s privacy awareness, ordinary truth discovery can no longer meet the current user demand for privacy protection, and solving the privacy problem is one of the critical challenges of truth discovery. A number of works have been proposed as the truth discovery mechanism of differential privacy. However, the privacy budget allocation in the known differential privacy truth discovery mechanisms does not consider the data precision differences of multi-source datasets and the anomalous results of small-value data after aggregation. We propose a precision division based on a differential privacy two-layer truth discovery framework to ensure privacy security while considering data precision and can also ensure high-accuracy truth discovery. The main challenges of this paper are how to obtain highly accurate truth values in sparse data scenarios without exposing the original values to the cloud server when performing error correction of aggregated anomalies after noise addition, as well as to improve the precision of truth value estimation of perturbed streaming data based on the refinement of the privacy protection level based on the accuracy of the data. Specifically, we first formulate a data-sampling algorithm to get the data precision of different users and to sieve out the anomalous data and duplicate data to obtain the quality of user-uploaded data. Then, we formulate a new privacy budget allocation mechanism, which synthesizes the sampling situation during data preprocessing and fully considers the data precision to quantify user privacy and turn it into specific values. We provide a quadratic truth discovery mechanism based on a predictive interpolation algorithm when dealing with small-value data, ensuring the reliability of small data aggregation results. We demonstrate that our framework achieves differential privacy for user-supplied data while we conduct extensive experiments on three real-world datasets to prove the effectiveness of our system framework.
期刊介绍:
Computers & Security is the most respected technical journal in the IT security field. With its high-profile editorial board and informative regular features and columns, the journal is essential reading for IT security professionals around the world.
Computers & Security provides you with a unique blend of leading edge research and sound practical management advice. It is aimed at the professional involved with computer security, audit, control and data integrity in all sectors - industry, commerce and academia. Recognized worldwide as THE primary source of reference for applied research and technical expertise it is your first step to fully secure systems.