scholarly journals From Centralized Protection to Distributed Edge Collaboration: A Location Difference-Based Privacy-Preserving Framework for Mobile Crowdsensing

2021 ◽  
Vol 2021 ◽  
pp. 1-18
Author(s):  
Zihao Shao ◽  
Huiqiang Wang ◽  
Yifan Zou ◽  
Zihan Gao ◽  
Hongwu Lv
Keyword(s):  
Location Privacy ◽  
Privacy Preserving ◽  
Secure Computation ◽  
User Characteristics ◽  
User Privacy ◽  
Mobile Crowdsensing ◽  
Data Coding ◽  
Sensing Platform ◽  
Single Region ◽  
The Impact

Mobile Crowdsensing (MCS) has evolved into an effective and valuable paradigm to engage mobile users to sense and collect urban-scale information. However, users risk their location privacy while reporting data with actual sensing locations. Existing works of location privacy-preserving are primarily based on single-region location information, which rely on a trusted and centralized sensing platform and ignore the impact of regional differences on user privacy-preserving demands. To tackle this issue, we propose a Location Difference-Based Privacy-Preserving Framework (LDPF), leveraging the powerful edge servers deployed between users and the sensing platform to hide and manage users according to regional user characteristics. More specifically, for popular regions, based on the edge servers and the k-anonymity algorithm, we propose a Coordinate Transformation and Bit Commitment (CTBC) privacy-preserving method that effectively guarantees the privacy of location data without relying on a trusted sensing platform. For remote regions, based on a more realistic distance calculation mode, we design a Paillier Encryption Data Coding (PDC) privacy-preserving method that realizes the secure computation for users’ location and prevents malicious users from deceiving. The theoretical analysis and simulation results demonstrate the security and efficiency of the proposed framework in location difference-based privacy-preserving.

Location Privacy-Preserving Task Recommendation with Geometric Range Query in Mobile Crowdsensing

2021 ◽  
pp. 1-1
Author(s):  
Chuan Zhang ◽  
Liehuang Zhu ◽  
Chang Xu ◽  
Jianbing Ni ◽  
Cheng Huang ◽  
...  
Keyword(s):  
Location Privacy ◽  
Range Query ◽  
Privacy Preserving ◽  
Mobile Crowdsensing

scholarly journals An Efficient and Privacy-Preserving Multiuser Cloud-Based LBS Query Scheme

2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Lu Ou ◽  
Hui Yin ◽  
Zheng Qin ◽  
Sheng Xiao ◽  
Guangyi Yang ◽  
...  
Keyword(s):  
Location Privacy ◽  
Security Analysis ◽  
Privacy Preserving ◽  
Location Based Services ◽  
Third Party ◽  
Location Information ◽  
User Privacy ◽  
User Revocation ◽  
And Performance ◽  
Cloud Infrastructures

Location-based services (LBSs) are increasingly popular in today’s society. People reveal their location information to LBS providers to obtain personalized services such as map directions, restaurant recommendations, and taxi reservations. Usually, LBS providers offer user privacy protection statement to assure users that their private location information would not be given away. However, many LBSs run on third-party cloud infrastructures. It is challenging to guarantee user location privacy against curious cloud operators while still permitting users to query their own location information data. In this paper, we propose an efficient privacy-preserving cloud-based LBS query scheme for the multiuser setting. We encrypt LBS data and LBS queries with a hybrid encryption mechanism, which can efficiently implement privacy-preserving search over encrypted LBS data and is very suitable for the multiuser setting with secure and effective user enrollment and user revocation. This paper contains security analysis and performance experiments to demonstrate the privacy-preserving properties and efficiency of our proposed scheme.

scholarly journals Location Privacy-Preserving Task Allocation for Mobile Crowdsensing with Differential Geo-Obfuscation

2017 ◽  
Author(s):  
Leye Wang ◽  
Dingqi Yang ◽  
Xiao Han ◽  
Tianben Wang ◽  
Daqing Zhang ◽  
...  
Keyword(s):  
Task Allocation ◽  
Location Privacy ◽  
Privacy Preserving ◽  
Mobile Crowdsensing

Privacy-preserving mechanisms for location privacy in mobile crowdsensing: A survey

2022 ◽  
pp. 103315
Author(s):  
Jong Wook Kim ◽  
Kennedy Edemacu ◽  
Beakcheol Jang
Keyword(s):  
Location Privacy ◽  
Privacy Preserving ◽  
Mobile Crowdsensing

Location Privacy-Preserving Data Recovery for Mobile Crowdsensing

2018 ◽  
Vol 2 (3) ◽  
pp. 1-23 ◽  
Author(s):  
Tongqing Zhou ◽  
Zhiping Cai ◽  
Bin Xiao ◽  
Leye Wang ◽  
Ming Xu ◽  
...  
Keyword(s):  
Location Privacy ◽  
Privacy Preserving ◽  
Data Recovery ◽  
Mobile Crowdsensing

An Efficient Dummy-Based Location Privacy-Preserving Scheme for Internet of Things Services

Information ◽  
2019 ◽  
Vol 10 (9) ◽  
pp. 278
Author(s):  
Yongwen Du ◽  
Gang Cai ◽  
Xuejun Zhang ◽  
Ting Liu ◽  
Jinghua Jiang
Keyword(s):  
Internet Of Things ◽  
Location Privacy ◽  
Side Information ◽  
Rapid Development ◽  
Security Analysis ◽  
Privacy Preserving ◽  
Location Based Services ◽  
User Privacy ◽  
Computational Costs ◽  
Smart Mobile

With the rapid development of GPS-equipped smart mobile devices and mobile computing, location-based services (LBS) are increasing in popularity in the Internet of Things (IoT). Although LBS provide enormous benefits to users, they inevitably introduce some significant privacy concerns. To protect user privacy, a variety of location privacy-preserving schemes have been recently proposed. Among these schemes, the dummy-based location privacy-preserving (DLP) scheme is a widely used approach to achieve location privacy for mobile users. However, the computation cost of the existing dummy-based location privacy-preserving schemes is too high to meet the practical requirements of resource-constrained IoT devices. Moreover, the DLP scheme is inadequate to resist against an adversary with side information. Thus, how to effectively select a dummy location is still a challenge. In this paper, we propose a novel lightweight dummy-based location privacy-preserving scheme, named the enhanced dummy-based location privacy-preserving(Enhanced-DLP) to address this challenge by considering both computational costs and side information. Specifically, the Enhanced-DLP adopts an improved greedy scheme to efficiently select dummy locations to form a k-anonymous set. A thorough security analysis demonstrated that our proposed Enhanced-DLP can protect user privacy against attacks. We performed a series of experiments to verify the effectiveness of our Enhanced-DLP. Compared with the existing scheme, the Enhanced-DLP can obtain lower computational costs for the selection of a dummy location and it can resist side information attacks. The experimental results illustrate that the Enhanced-DLP scheme can effectively be applied to protect the user’s location privacy in IoT applications and services.

scholarly journals CrowdBLPS: A Blockchain-Based Location-Privacy-Preserving Mobile Crowdsensing System

2020 ◽  
Vol 16 (6) ◽  
pp. 4206-4218 ◽  
Author(s):  
Shihong Zou ◽  
Jinwen Xi ◽  
Honggang Wang ◽  
Guoai Xu
Keyword(s):  
Location Privacy ◽  
Privacy Preserving ◽  
Mobile Crowdsensing

scholarly journals COVID-19 Vulnerability Map Construction via Location Privacy Preserving Mobile Crowdsourcing

2020 ◽  
Author(s):  
Rui Chen ◽  
Liang Li ◽  
Jeffrey Jiarui Chen ◽  
Ronghui Hou ◽  
Yanmin Gong ◽  
...  
Keyword(s):  
Location Privacy ◽  
Privacy Preserving ◽  
Mobile Crowdsourcing ◽  
Map Construction ◽  
Vulnerability Map

scholarly journals Exploring the Impact of Technology-Facilitated Abuse and Its Relationship with Domestic Violence: A Qualitative Study on Experts’ Perceptions

2021 ◽  
Vol 8 ◽  
pp. 233339362110281
Author(s):  
Renee Fiolet ◽  
Cynthia Brown ◽  
Molly Wellington ◽  
Karen Bentley ◽  
Kelsey Hegarty
Keyword(s):  
Domestic Violence ◽  
Service Providers ◽  
Structured Interviews ◽  
Specialist Service ◽  
Data Coding ◽  
Integral Role ◽  
Impact Of Technology ◽  
The Impact ◽  
The Relationship ◽  
Inductive Thematic Analysis

Technology-facilitated abuse can be a serious form of domestic violence. Little is known about the relationship between technology-facilitated abuse and other types of domestic violence, or the impact technology-facilitated abuse has on survivors. The aim of this interpretative descriptive study is to understand domestic violence specialist service providers’ perspectives on the impact of technology-facilitated abuse, and the link between technology-facilitated abuse and other forms of domestic violence. A qualitative approach using 15 semi-structured interviews were undertaken with Australian domestic violence specialist practitioners, and three themes were identified through data coding using inductive thematic analysis. Another form of control describes technology-facilitated abuse behaviors as enacting controlling behaviors using new mediums. Amplifies level of fear characterizes the impact of technology-facilitated abuse. A powerful tool to engage others describes opportunities technology offers perpetrators to abuse through engaging others. Findings highlight technology-facilitated abuse’s complexity and integral role in domestic violence and can assist clinicians to understand the impact and harm that can result from technology-facilitated abuse.

Privacy Preserving Deep Learning based on Multi-Party Secure Computation: A Survey

2021 ◽  
pp. 1-1
Author(s):  
Qiao Zhang ◽  
Chunsheng Xin ◽  
Hongyi Wu
Keyword(s):  
Deep Learning ◽  
Privacy Preserving ◽  
Secure Computation
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