حفظ حریم خصوصی مکانی در جمع سپاری
ترجمه نشده

حفظ حریم خصوصی مکانی در جمع سپاری

عنوان فارسی مقاله: حفظ حریم خصوصی مکانی در جمع سپاری فضایی تحت کنترل کیفیت
عنوان انگلیسی مقاله: Preserving Location Privacy in Spatial Crowdsourcing Under Quality Control
مجله/کنفرانس: دسترسی – IEEE Access
رشته های تحصیلی مرتبط: مدیریت، مهندسی کامپیوتر
گرایش های تحصیلی مرتبط: مدیریت منابع اطلاعاتی، امنیت اطلاعات
کلمات کلیدی فارسی: جمع سپاری فضایی، حریم خصوصی مکانی مبهم کارکنان، کنترل کیفیت، الگوریتم حداکثرسازی انتظار (EM)
کلمات کلیدی انگلیسی: Spatial crowdsourcing, obfuscation location privacy of workers, quality control, EM algorithm
نوع نگارش مقاله: مقاله پژوهشی (Research Article)
شناسه دیجیتال (DOI): https://doi.org/10.1109/ACCESS.2019.2949409
دانشگاه: School of Maritime Economics and Management, Dalian Maritime University, Dalian 116026, China
صفحات مقاله انگلیسی: 9
ناشر: آی تریپل ای - IEEE
نوع ارائه مقاله: ژورنال
نوع مقاله: ISI
سال انتشار مقاله: 2019
ایمپکت فاکتور: 4.641 در سال 2018
شاخص H_index: 56 در سال 2019
شاخص SJR: 0.609 در سال 2018
شناسه ISSN: 2169-3536
شاخص Quartile (چارک): Q2 در سال 2018
فرمت مقاله انگلیسی: PDF
وضعیت ترجمه: ترجمه نشده است
قیمت مقاله انگلیسی: رایگان
آیا این مقاله بیس است: خیر
آیا این مقاله مدل مفهومی دارد: ندارد
آیا این مقاله پرسشنامه دارد: ندارد
آیا این مقاله متغیر دارد: ندارد
کد محصول: E13925
رفرنس: دارای رفرنس در داخل متن و انتهای مقاله
فهرست مطالب (انگلیسی)

Abstract

I. Introduction

II. Problem Statement

III. Obfuscation Arithmetic

IV. Quality Control of SC

V. Numerical Experiments

Authors

Figures

References

بخشی از مقاله (انگلیسی)

Abstract

Emerging spatial crowdsourcing (SC) provides an approach for collecting and analyzing spatiotemporal information from intelligent transportation systems. However, the exposure of massive location privacy to potential adversaries for the purpose of quality control makes workers more vulnerable. To protect workers’ location privacy, an obfuscation scheme is proposed to incorporate uncertainties into the SC quality control problem through obfuscating the standard location data in terms of both space and time. Two measures, location entropy and results accuracy, are used to evaluate the performance of location privacy protection. We theoretically and experimentally confirm the security and accuracy of the obfuscation approach. The results of experiments show that: a) hiding workers’ location from the requester reduces the quality of SC; and b) obfuscation arithmetic with appropriate obfuscation coefficients protects workers’ location privacy with little effect on SC quality. Under the protection of this obfuscation scheme, the new system provides better security and similar quality compared to the existing SC system.

Introduction

The crowdsourcing model is frequently used to gather data in intelligent transportation systems (ITS) applications e.g., avoiding traffic congestion. The new mechanism for collecting and analyzing spatiotemporal information is spatial crowdsourcing (SC), and this mechanism exploits a large volume of vehicles and their mobility. In the SC platform, a requester outsources a set of spatiotemporal tasks to a set of workers with mobile devices, and workers perform tasks after physically traveling to places of interest [1], e.g. the outsourcer requests workers in cars to collect real-time traffic information on roads Moreover, to assist in checking workers’ submissions, some existing SC platforms require workers to disclose their immediate locations along with the taskspecified submission to the requester, who may be a potential adversary seeking to attack the location privacy of individual workers [2]. In practice, some malicious requesters may collect private information on worker locations through deliberately designed SC tasks. In addition, a crowdsourcing task is a kind of micro task, so a worker may submit numerous tasks with location information in a short period of time. Revealing workers’ precise locations may allow an adversary to infer sensitive information and even to stalk or mug workers [3]. Hence, protecting location privacy is an essential aspect of SC, since workers will not agree to participate in spatial tasks if there is a possibility of a privacy breach.