scholarly journals Protecting the Moving User’s Locations by Combining Differential Privacy and k -Anonymity under Temporal Correlations in Wireless Networks

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Weiqi Zhang ◽  
Guisheng Yin ◽  
Yuhai Sha ◽  
Jishen Yang
Keyword(s):  
Privacy Protection ◽  
Differential Privacy ◽  
Service Providers ◽  
Personal Information ◽  
Rapid Development ◽  
High Standard ◽  
Location Based Services ◽  
Third Party ◽  
The Third ◽  
Temporal Correlations

The rapid development of the Global Positioning System (GPS) devices and location-based services (LBSs) facilitates the collection of huge amounts of personal information for the untrusted/unknown LBS providers. This phenomenon raises serious privacy concerns. However, most of the existing solutions aim at locating interference in the static scenes or in a single timestamp without considering the correlation between location transfer and time of moving users. In this way, the solutions are vulnerable to various inference attacks. Traditional privacy protection methods rely on trusted third-party service providers, but in reality, we are not sure whether the third party is trustable. In this paper, we propose a systematic solution to preserve location information. The protection provides a rigorous privacy guarantee without the assumption of the credibility of the third parties. The user’s historical trajectory information is used as the basis of the hidden Markov model prediction, and the user’s possible prospective location is used as the model output result to protect the user’s trajectory privacy. To formalize the privacy-protecting guarantee, we propose a new definition, L&A-location region, based on k -anonymity and differential privacy. Based on the proposed privacy definition, we design a novel mechanism to provide a privacy protection guarantee for the users’ identity trajectory. We simulate the proposed mechanism based on a dataset collected in real practice. The result of the simulation shows that the proposed algorithm can provide privacy protection to a high standard.

A Survey on Privacy Preservation in Location-Based Mobile Business

2021 ◽  
Vol 13 (1) ◽  
pp. 20-39
Author(s):  
Ahmed Aloui ◽  
Okba Kazar
Keyword(s):  
Privacy Protection ◽  
Privacy Preservation ◽  
Service Providers ◽  
Personal Information ◽  
Third Party ◽  
Mobile Business ◽  
Protection Measures ◽  
System Architectures ◽  
Protection Goals ◽  
Privacy Issues

In mobile business (m-business), a client sends its exact locations to service providers. This data may involve sensitive and private personal information. As a result, misuse of location information by the third party location servers creating privacy issues for clients. This paper provides an overview of the privacy protection techniques currently applied by location-based mobile business. The authors first identify different system architectures and different protection goals. Second, this article provides an overview of the basic principles and mechanisms that exist to protect these privacy goals. In a third step, the authors provide existing privacy protection measures.

scholarly journals A Symbiotic Relationship Based Leader Approach for Privacy Protection in Location Based Services

2020 ◽  
Vol 9 (6) ◽  
pp. 408
Author(s):  
Hosam Alrahhal ◽  
Mohamad Shady Alrahhal ◽  
Razan Jamous ◽  
Kamal Jambi
Keyword(s):  
Privacy Protection ◽  
Personal Information ◽  
Location Based Services ◽  
Third Party ◽  
Symbiotic Relationship ◽  
Wireless Devices ◽  
Daily Growth ◽  
Location Selection ◽  
The Moment ◽  
The Internet Of Things

Location-based services (LBS) form the main part of the Internet of Things (IoT) and have received a significant amount of attention from the research community as well as application users due to the popularity of wireless devices and the daily growth in users. However, there are several risks associated with the use of LBS-enabled applications, as users are forced to send their queries based on their real-time and actual location. Attacks could be applied by the LBS server itself or by its maintainer, which consequently may lead to more serious issues such as the theft of sensitive and personal information about LBS users. Due to this fact, complete privacy protection (location and query privacy protection) is a critical problem. Collaborative (cache-based) approaches are used to prevent the LBS application users from connecting to the LBS server (malicious parties). However, no robust trust approaches have been provided to design a trusted third party (TTP), which prevents LBS users from acting as an attacker. This paper proposed a symbiotic relationship-based leader approach to guarantee complete privacy protection for users of LBS-enabled applications. Specifically, it introduced the mutual benefit underlying the symbiotic relationship, dummies, and caching concepts to avoid dealing with untrusted LBS servers and achieve complete privacy protection. In addition, the paper proposed a new privacy metric to predict the closeness of the attacker to the moment of her actual attack launch. Compared to three well-known approaches, namely enhanced dummy location selection (enhanced-DLS), hiding in a mobile crowd, and caching-aware dummy selection algorithm (enhanced-CaDSA), our experimental results showed better performance in terms of communication cost, resistance against inferences attacks, and cache hit ratio.

scholarly journals An Adaptive Grid and Incentive Mechanism for Personalized Differentially Private Location Data in the Local Setting

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Kangsoo Jung ◽  
Seog Park
Keyword(s):  
Privacy Protection ◽  
Differential Privacy ◽  
Incentive Mechanism ◽  
Location Based Services ◽  
Third Party ◽  
Location Data ◽  
Local Setting ◽  
Private Location ◽  
Safe Region ◽  
Region Size

With the proliferation of wireless communication and mobile devices, various location-based services are emerging. For the growth of the location-based services, more accurate and various types of personal location data are required. However, concerns about privacy violations are a significant obstacle to obtain personal location data. In this paper, we propose a local differential privacy scheme in an environment where there is no trusted third party to implement privacy protection techniques and incentive mechanisms to motivate users to provide more accurate location data. The proposed local differential privacy scheme allows a user to set a personalized safe region that he/she can disclose and then perturb the user’s location within the safe region. It is the way to satisfy the user’s various privacy requirements and improve data utility. The proposed incentive mechanism has two models, and both models pay the incentive differently according to the user’s safe region size to motivate to set a more precise safe region. We verify the proposed local differential privacy algorithm and incentive mechanism can satisfy the privacy protection level while achieving the desirable utility through the experiment.

ILSSPP: Intelligent location sharing system with privacy preserving features for smart cities

2021 ◽  
pp. 1-12
Author(s):  
Gokay Saldamli ◽  
Richard Chow ◽  
Hongxia Jin
Keyword(s):  
Service Providers ◽  
Smart Cities ◽  
User Interaction ◽  
Privacy Preserving ◽  
Location Based Services ◽  
Third Party ◽  
Location Information ◽  
Fine Grained ◽  
Location Sharing ◽  
Social Network Structure

Social networking services are increasingly accessed through mobile devices. This trend has prompted services such as Facebook and Google+to incorporate location as a de facto feature of user interaction. At the same time, services based on location such as Foursquare and Shopkick are also growing as smartphone market penetration increases. In fact, this growth is happening despite concerns (growing at a similar pace) about security and third-party use of private location information (e.g., for advertising). Nevertheless, service providers have been unwilling to build truly private systems in which they do not have access to location information. In this paper, we describe an architecture and a trial implementation of a privacy-preserving location sharing system called ILSSPP. The system protects location information from the service provider and yet enables fine grained location-sharing. One main feature of the system is to protect an individual’s social network structure. The pattern of location sharing preferences towards contacts can reveal this structure without any knowledge of the locations themselves. ILSSPP protects locations sharing preferences through protocol unification and masking. ILSSPP has been implemented as a standalone solution, but the technology can also be integrated into location-based services to enhance privacy.

scholarly journals Decentralized collaborative TTP free approach for privacy preservation in location based services

2019 ◽  
Vol 9 (6) ◽  
pp. 5376
Author(s):  
Ajaysinh Devendrasinh Rathod ◽  
Saurabh Shah ◽  
Vivaksha J. Jariwala
Keyword(s):  
Privacy Preservation ◽  
Location Privacy ◽  
Emergency Services ◽  
Personal Information ◽  
Personal Digital Assistant ◽  
Minimum Cost ◽  
Location Based Services ◽  
Third Party ◽  
Trusted Third Party ◽  
Recent Trends

In recent trends, growth of location based services have been increased due to the large usage of cell phones, personal digital assistant and other devices like location based navigation, emergency services, location based social networking, location based advertisement, etc. Users are provided with important information based on location to the service provider that results the compromise with their personal information like user’s identity, location privacy etc. To achieve location privacy of the user, cryptographic technique is one of the best technique which gives assurance. Location based services are classified as Trusted Third Party (TTP) & without Trusted Third Party that uses cryptographic approaches. TTP free is one of the prominent approach in which it uses peer-to-peer model. In this approach, important users mutually connect with each other to form a network to work without the use of any person/server. There are many existing approaches in literature for privacy preserving location based services, but their solutions are at high cost or not supporting scalability.  In this paper, our aim is to propose an approach along with algorithms that will help the location based services (LBS) users to provide location privacy with minimum cost and improve scalability.

Location-Based Services (LBS) in Micro-Scale Navigation

2009 ◽  
Vol 1 (4) ◽  
pp. 51-71 ◽  
Author(s):  
Suleiman Almasri ◽  
Muhammad Alnabhan ◽  
Ziad Hunaiti ◽  
Eliamani Sedoyeka
Keyword(s):  
Service Providers ◽  
Experimental Tests ◽  
Location Based Services ◽  
Third Party ◽  
Mobile Technologies ◽  
Large Field ◽  
Battery Lifetime ◽  
Micro Scale ◽  
Multimedia Contents ◽  
Recent Developments

Pedestrians LBS are accessible by hand-held devices and become a large field of energetic research since the recent developments in wireless communication, mobile technologies and positioning techniques. LBS applications provide services like finding the neighboring facility within a certain area such as the closest restaurants, hospital, or public telephone. With the increased demand for richer mobile services, LBS propose a promising add-on to the current services offered by network operators and third-party service providers such as multimedia contents. The performance of LBS systems is directly affected by each component forming its architecture. Firstly, the end-user mobile device is still experiencing a lack of enough storage, limitations in CPU capabilities and short battery lifetime. Secondly, the mobile wireless network is still having problems with the size of bandwidth, packet loss, congestions and delay. Additionally, in spite of the fact that GPS is the most accurate navigation system, there are still some issues in micro scale navigation, mainly availability and accuracy. Finally, LBS server which hosts geographical and users information is experiencing difficulties in managing the huge size of data which causes a long query processing time. This paper presents a technical investigation and analysis of the performance of each component of LBS system for pedestrian navigation, through conducting several experimental tests in different locations. The results of this investigation have pinpointed the weaknesses of the system in micro-scale environments. In addition, this paper proposes a group of solutions and recommendations for most of these shortcomings.

scholarly journals A Context-Aware Location Differential Perturbation Scheme for Privacy-Aware Users in Mobile Environment

2018 ◽  
Vol 2018 ◽  
pp. 1-15 ◽  
Author(s):  
Xuejun Zhang ◽  
Haiyan Huang ◽  
Shan Huang ◽  
Qian Chen ◽  
Tao Ju ◽  
...  
Keyword(s):  
Location Privacy ◽  
Differential Privacy ◽  
Location Based Services ◽  
Third Party ◽  
Background Information ◽  
Perturbation Scheme ◽  
Context Aware ◽  
Hilbert Curve ◽  
Mobile Environment ◽  
Retrieval Accuracy

The proliferation of location-based services, representative services for the mobile networks, has posed a serious threat to users’ privacy. In the literature, several privacy mechanisms have been proposed to preserve location privacy. Location obfuscation enforced using cloaking region is a widely used technique to achieve location privacy. However, it requires a trusted third-party (TTP) and cannot sufficiently resist various inference attacks based on background information and thus is vulnerable to location privacy breach. In this paper, we propose a context-aware location privacy-preserving solution with differential perturbations, which can enhance the user’s location privacy without requiring a TTP. Our scheme utilizes the modified Hilbert curve to project every 2-d location of the user in the considered map to 1-d space and randomly generates the reasonable perturbation by adding Laplace noise via differential privacy. In order to solve the resource limitation of mobile devices, we use a quad-tree based scheme to transform and store the user context information as bit stream which achieves the high compression ratio and supports efficient retrieval. Security analysis shows that our proposed scheme can effectively preserve the location privacy. Experimental evaluation shows that our scheme retrieval accuracy is increased by an average of 15.4% compared with the scheme using standard Hilbert curve. Our scheme can provide strong privacy guarantees with a bounded accuracy loss while improving retrieval accuracy.

scholarly journals Data Privacy Protection Based on Micro Aggregation with Dynamic Sensitive Attribute Updating

Sensors ◽  
2018 ◽  
Vol 18 (7) ◽  
pp. 2307 ◽  
Author(s):  
Yancheng Shi ◽  
Zhenjiang Zhang ◽  
Han-Chieh Chao ◽  
Bo Shen
Keyword(s):  
Privacy Protection ◽  
Data Privacy ◽  
Large Scale ◽  
Data Centers ◽  
Personal Information ◽  
Rapid Development ◽  
Data Availability ◽  
Personal Privacy ◽  
Data Anonymization ◽  
Data Privacy Protection

With the rapid development of information technology, large-scale personal data, including those collected by sensors or IoT devices, is stored in the cloud or data centers. In some cases, the owners of the cloud or data centers need to publish the data. Therefore, how to make the best use of the data in the risk of personal information leakage has become a popular research topic. The most common method of data privacy protection is the data anonymization, which has two main problems: (1) The availability of information after clustering will be reduced, and it cannot be flexibly adjusted. (2) Most methods are static. When the data is released multiple times, it will cause personal privacy leakage. To solve the problems, this article has two contributions. The first one is to propose a new method based on micro-aggregation to complete the process of clustering. In this way, the data availability and the privacy protection can be adjusted flexibly by considering the concepts of distance and information entropy. The second contribution of this article is to propose a dynamic update mechanism that guarantees that the individual privacy is not compromised after the data has been subjected to multiple releases, and minimizes the loss of information. At the end of the article, the algorithm is simulated with real data sets. The availability and advantages of the method are demonstrated by calculating the time, the average information loss and the number of forged data.

scholarly journals Cache-Based Privacy Preserving Solution for Location and Content Protection in Location-Based Services

Sensors ◽  
2020 ◽  
Vol 20 (16) ◽  
pp. 4651
Author(s):  
Yuanbo Cui ◽  
Fei Gao ◽  
Wenmin Li ◽  
Yijie Shi ◽  
Hua Zhang ◽  
...  
Keyword(s):  
Location Privacy ◽  
Personal Information ◽  
Single Point ◽  
Privacy Preserving ◽  
Location Based Services ◽  
Third Party ◽  
Content Protection ◽  
Query Privacy ◽  
Service Data

Location-Based Services (LBSs) are playing an increasingly important role in people’s daily activities nowadays. While enjoying the convenience provided by LBSs, users may lose privacy since they report their personal information to the untrusted LBS server. Although many approaches have been proposed to preserve users’ privacy, most of them just focus on the user’s location privacy, but do not consider the query privacy. Moreover, many existing approaches rely heavily on a trusted third-party (TTP) server, which may suffer from a single point of failure. To solve the problems above, in this paper we propose a Cache-Based Privacy-Preserving (CBPP) solution for users in LBSs. Different from the previous approaches, the proposed CBPP solution protects location privacy and query privacy simultaneously, while avoiding the problem of TTP server by having users collaborating with each other in a mobile peer-to-peer (P2P) environment. In the CBPP solution, each user keeps a buffer in his mobile device (e.g., smartphone) to record service data and acts as a micro TTP server. When a user needs LBSs, he sends a query to his neighbors first to seek for an answer. The user only contacts the LBS server when he cannot obtain the required service data from his neighbors. In this way, the user reduces the number of queries sent to the LBS server. We argue that the fewer queries are submitted to the LBS server, the less the user’s privacy is exposed. To users who have to send live queries to the LBS server, we employ the l-diversity, a powerful privacy protection definition that can guarantee the user’s privacy against attackers using background knowledge, to further protect their privacy. Evaluation results show that the proposed CBPP solution can effectively protect users’ location and query privacy with a lower communication cost and better quality of service.

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