scholarly journals Quantifying and protecting location privacy

2015 ◽  
Vol 57 (4) ◽  
Author(s):  
Reza Shokri
Keyword(s):  
Location Privacy ◽  
Stackelberg Game ◽  
Human Mobility ◽  
Estimation Error ◽  
Location Based Services ◽  
Sensitive Information ◽  
User Privacy ◽  
Inference Problem ◽  
Protection Mechanisms ◽  
Channel Information

AbstractThis thesis addresses the timely concern of protecting privacy in the age of big data. We identify the two following problems as the fundamental problems in computational privacy: (i) consistently quantifying privacy in different systems and (ii) optimally protecting privacy using obfuscation mechanisms. We cast the problem of quantifying privacy as computing the estimation error in a statistical (Bayesian) inference problem, where an adversary combines his observation, background knowledge and side channel information to estimate the user's sensitive information. This enables us to evaluate privacy of users in different systems, and consistently compare the effectiveness of different privacy protection mechanisms. We also formulate the problem of optimizing user privacy while respecting data utility as an interactive optimization problem (Bayesian Stackelberg game), where both user and adversary want to maximize their own objectives which are in conflict with each other. We apply our methodologies to quantifying and protecting location privacy in location-based services. We also provide an open-source tool, named Location-Privacy and Mobility Meter (LPM), that enables researchers to learn and analyze human mobility models as well as evaluating and comparing different location-privacy preserving mechanisms.

scholarly journals Privacy-Enhancing Preferential LBS Query for Mobile Social Network Users

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Madhuri Siddula ◽  
Yingshu Li ◽  
Xiuzhen Cheng ◽  
Zhi Tian ◽  
Zhipeng Cai
Keyword(s):  
Social Networking Sites ◽  
Location Privacy ◽  
Location Based Services ◽  
Sensitive Information ◽  
Context Aware ◽  
Friendship Networks ◽  
Mobile Social Network ◽  
User Privacy ◽  
The Cost ◽  
Privacy Issues

While social networking sites gain massive popularity for their friendship networks, user privacy issues arise due to the incorporation of location-based services (LBS) into the system. Preferential LBS takes a user’s social profile along with their location to generate personalized recommender systems. With the availability of the user’s profile and location history, we often reveal sensitive information to unwanted parties. Hence, providing location privacy to such preferential LBS requests has become crucial. However, the current technologies focus on anonymizing the location through granularity generalization. Such systems, although provides the required privacy, come at the cost of losing accurate recommendations. Hence, in this paper, we propose a novel location privacy-preserving mechanism that provides location privacy through k-anonymity and provides the most accurate results. Experimental results that focus on mobile users and context-aware LBS requests prove that the proposed method performs superior to the existing methods.

scholarly journals Privacy Preserving Spatio-Temporal Databases Based on k-Anonymity

2020 ◽  
Vol 3 (SI1) ◽  
pp. SI82-SI94
Author(s):  
Anh Tuan Truong
Keyword(s):  
Data Mining ◽  
Privacy Protection ◽  
Location Privacy ◽  
Location Based Services ◽  
Sensitive Information ◽  
User Privacy ◽  
Location Data ◽  
Related Information ◽  
Spatio Temporal ◽  
Location Privacy Protection

The development of location-based services and mobile devices has lead to an increase in the location data. Through the data mining process, some valuable information can be discovered from location data. In the other words, an attacker may also extract some private (sensitive) information of the user and this may make threats against the user privacy. Therefore, location privacy protection becomes an important requirement to the success in the development of location-based services. In this paper, we propose a grid-based approach as well as an algorithm to guarantee k-anonymity, a well-known privacy protection approach, in a location database. The proposed approach considers only the information that has significance for the data mining process while ignoring the un-related information. The experiment results show the effectiveness of the proposed approach in comparison with the literature ones.

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.

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 A Method of Location Privacy Protection in Road Network Environment

2018 ◽  
Vol 173 ◽  
pp. 03048 ◽  
Author(s):  
Jianjun Wen ◽  
Zhao Li
Keyword(s):  
Privacy Protection ◽  
Road Network ◽  
Location Privacy ◽  
Location Based Services ◽  
Network Environment ◽  
User Privacy ◽  
Widespread Application ◽  
Location Service ◽  
Server Side ◽  
User Query

With the widespread application of location-based services, users 'privacy concerns have become the focus of users' attention. Based on the k-anonymity method and the SpaceTwist algorithm, this paper proposes a method of incremental inquiry user privacy protection. The method preliminarily anonymizes the user's location information and points of interest on the client side, On the anonymous server side, combining the road network environment with the latitude and longitude grid generates the minimum anonymous area of random loop, instead of the user initiating incremental inquiry to the location service provider, Anonymous zones ensure k-anonymity for mobile users and road information to protect user privacy. Security and experimental analysis show that this scheme can improve the effectiveness of user query service while meeting the privacy requirements of users.

scholarly journals Nonexposure Accurate LocationK-Anonymity Algorithm in LBS

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Jinying Jia ◽  
Fengli Zhang
Keyword(s):  
Privacy Protection ◽  
Location Privacy ◽  
Location Based Services ◽  
Experimental Results ◽  
Location Information ◽  
User Privacy ◽  
Spatial Region ◽  
Exact Location ◽  
Current Location ◽  
Location Privacy Protection

This paper tackles location privacy protection in current location-based services (LBS) where mobile users have to report their exact location information to an LBS provider in order to obtain their desired services. Location cloaking has been proposed and well studied to protect user privacy. It blurs the user’s accurate coordinate and replaces it with a well-shaped cloaked region. However, to obtain such an anonymous spatial region (ASR), nearly all existent cloaking algorithms require knowing the accurate locations of all users. Therefore, location cloaking without exposing the user’s accurate location to any party is urgently needed. In this paper, we present such two nonexposure accurate location cloaking algorithms. They are designed forK-anonymity, and cloaking is performed based on the identifications (IDs) of the grid areas which were reported by all the users, instead of directly on their accurate coordinates. Experimental results show that our algorithms are more secure than the existent cloaking algorithms, need not have all the users reporting their locations all the time, and can generate smaller ASR.

New Blind Filter Protocol: An Improved Privacy-Preserving Scheme for Location-Based Services

2020 ◽  
Vol 63 (12) ◽  
pp. 1886-1903
Author(s):  
Zhidan Li ◽  
Wenmin Li ◽  
Fei Gao ◽  
Ping Yu ◽  
Hua Zhang ◽  
...  
Keyword(s):  
Service Provider ◽  
Location Privacy ◽  
Security Analysis ◽  
Location Based Services ◽  
Communication Overhead ◽  
Location Based Service ◽  
User Privacy ◽  
Circular Area ◽  
Communication Costs ◽  
Point Of Interest

Abstract Location-based services have attracted much attention in both academia and industry. However, protecting user’s privacy while providing accurate service for users remains challenging. In most of the existing research works, a semi-trusted proxy is employed to act on behalf of a user to minimize the computation and communication costs of the user. However, user privacy, e.g. location privacy, cannot be protected against the proxy. In this paper, we design a new blind filter protocol where a user can employ a semi-trusted proxy to determine whether a point of interest is within a circular area centered at the user’s location. During the protocol, neither the proxy nor the location-based service provider can obtain the location of the user and the query results. Moreover, each type of query is controlled by an access tree and only the users whose attributes satisfy this access tree can complete the specific type of query. Security analysis and efficiency experiments validate that the proposed protocol is secure and efficient in terms of the computation and communication overhead.

scholarly journals A Novel Location Privacy-Preserving Approach Based on Blockchain

Sensors ◽  
2020 ◽  
Vol 20 (12) ◽  
pp. 3519 ◽  
Author(s):  
Ying Qiu ◽  
Yi Liu ◽  
Xuan Li ◽  
Jiahui Chen
Keyword(s):  
Privacy Protection ◽  
Location Privacy ◽  
User Participation ◽  
Location Based Services ◽  
Third Party ◽  
User Privacy ◽  
Protection Method ◽  
Location Privacy Protection ◽  
Reward Mechanism

Location-based services (LBS) bring convenience to people’s lives but are also accompanied with privacy leakages. To protect the privacy of LBS users, many location privacy protection algorithms were proposed. However, these algorithms often have difficulty to maintain a balance between service quality and user privacy. In this paper, we first overview the shortcomings of the existing two privacy protection architectures and privacy protection technologies, then we propose a location privacy protection method based on blockchain. Our method satisfies the principle of k-anonymity privacy protection and does not need the help of trusted third-party anonymizing servers. The combination of multiple private blockchains can disperse the user’s transaction records, which can provide users with stronger location privacy protection and will not reduce the quality of service. We also propose a reward mechanism to encourage user participation. Finally, we implement our approach in the Remix blockchain to show the efficiency, which further indicates the potential application prospect for the distributed network environment.

scholarly journals EDEN

2021 ◽  
Vol 5 (2) ◽  
pp. 1-25
Author(s):  
Besma Khalfoun ◽  
Sonia Ben Mokhtar ◽  
Sara Bouchenak ◽  
Vlad Nitu
Keyword(s):  
Location Privacy ◽  
Real Life ◽  
Difficult Problem ◽  
Mobile App ◽  
Sensitive Information ◽  
Sensing Applications ◽  
Crowd Sensing ◽  
Protection Mechanisms ◽  
The Right ◽  
Location Privacy Protection

Crowd sensing applications have demonstrated their usefulness in many real-life scenarios (e.g., air quality monitoring, traffic and noise monitoring). Preserving the privacy of crowd sensing app users is becoming increasingly important as the collected geo-located data may reveal sensitive information about these users (e.g., home, work places, political, religious, sexual preferences). In this context, a large variety of Location Privacy Protection Mechanisms (LPPMs) have been proposed. However, each LPPM comes with a given set of configuration parameters. The value of these parameters impacts not only the privacy level but also the utility of the resulting data. Choosing the right LPPM and the right configuration for reaching a satisfactory privacy vs. utility tradeoff is generally a difficult problem mobile app developers have to face. Solving this problem is commonly done by relying on a trusted proxy server to which raw geo-located traces are sent and privacy vs. utility assessment is performed enabling the selection of the best LPPM for each trace. In this paper we present EDEN, the first solution that selects automatically the best LPPM and its corresponding configuration without sending raw geo-located traces outside the user's device. We reach this objective by relying on a federated learning approach. The evaluation of EDEN on five real-world mobility datasets shows that EDEN outperforms state-of-the-art LPPMs reaching a better privacy vs. utility tradeoff.

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