scholarly journals Differential Privacy Location Protection Method Based on the Markov Model

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Hongtao Li ◽  
Yue Wang ◽  
Feng Guo ◽  
Jie Wang ◽  
Bo Wang ◽  
...  
Keyword(s):  
Markov Model ◽  
Location Privacy ◽  
Differential Privacy ◽  
Mobile Internet ◽  
Location Based Services ◽  
Data Availability ◽  
Internet Technology ◽  
Widespread Application ◽  
Location Data ◽  
Protection Method

Location-based services (LBS) have become an important research area with the rapid development of mobile Internet technology, GPS positioning technology, and the widespread application of smart phones and social networks. LBS can provide convenience and flexibility for the users’ daily life, but at the same time, it also brings security risks to the users’ privacy. Untrusted or malicious LBS servers can collect users’ location data through various ways and disclose it to the third party, thus causing users’ privacy leakage. In this paper, a differential privacy location protection method based on the Markov model for user’s location privacy is proposed. Firstly, the transition probability matrix between states of the n -order Markov model is used to predict the occurrence state and development trend of events; thereby, the user’s location is predicted, and then a location prediction algorithm based on the Markov model (LPAM) is proposed. Secondly, a location protection algorithm based on differential privacy (LPADP) is proposed, in which location privacy tree (LPT) is constructed according to the location data and the difficulty of retrieval, the two nodes with the largest predicted value of LPT are allocated with a reasonable privacy budget, and Laplace noise is added to protect location privacy. Theoretical analysis and experimental results show that the proposed method not only meets the requirements of differential privacy and protects location privacy effectively but also has high data availability and low time complexity.

scholarly journals Differential Privacy Location Protection Scheme Based on Hilbert Curve

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Jie Wang ◽  
Feng Wang ◽  
Hongtao Li
Keyword(s):  
Privacy Protection ◽  
Location Privacy ◽  
Differential Privacy ◽  
Dimensional Space ◽  
Location Based Services ◽  
Data Availability ◽  
Location Information ◽  
Hilbert Curve ◽  
One Dimensional ◽  
Protection Scheme

Location-based services (LBS) applications provide convenience for people’s life and work, but the collection of location information may expose users’ privacy. Since these collected data contain much private information about users, a privacy protection scheme for location information is an impending need. In this paper, a protection scheme DPL-Hc is proposed. Firstly, the users’ location on the map is mapped into one-dimensional space by using Hilbert curve mapping technology. Then, the Laplace noise is added to the location information of one-dimensional space for perturbation, which considers more than 70% of the nonlocation information of users; meanwhile, the disturbance effect is achieved by adding noise. Finally, the disturbed location is submitted to the service provider as the users’ real location to protect the users’ location privacy. Theoretical analysis and simulation results show that the proposed scheme can protect the users’ location privacy without the trusted third party effectively. It has advantages in data availability, the degree of privacy protection, and the generation time of anonymous data sets, basically achieving the balance between privacy protection and service quality.

scholarly journals Noise-added selection method for location-based service using differential privacy in Internet of Things

2019 ◽  
Vol 11 (1) ◽  
pp. 168781401882239 ◽  
Author(s):  
Zhimin Li ◽  
Haoze Lv ◽  
Zhaobin Liu
Keyword(s):  
Big Data ◽  
Internet Of Things ◽  
Privacy Protection ◽  
Location Privacy ◽  
Differential Privacy ◽  
Poor Performance ◽  
Data Availability ◽  
Location Information ◽  
Location Based Service ◽  
Protection Method

With the development of Internet of Things, many applications need to use people’s location information, resulting in a large amount of data need to be processed, called big data. In recent years, people propose many methods to protect privacy in the location-based service aspect. However, existing technologies have poor performance in big data area. For instance, sensor equipments such as smart phones with location record function may submit location information anytime and anywhere which may lead to privacy disclosure. Attackers can leverage huge data to achieve useful information. In this article, we propose noise-added selection algorithm, a location privacy protection method that satisfies differential privacy to prevent the data from privacy disclosure by attacker with arbitrary background knowledge. In view of Internet of Things, we maximize the availability of data and algorithm when protecting the information. In detail, we filter real-time location distribution information, use our selection mechanism for comparison and analysis to determine privacy-protected regions, and then perform differential privacy on them. As shown in the theoretical analysis and the experimental results, the proposed method can achieve significant improvements in security, privacy, and complete a perfect balance between privacy protection level and data availability.

scholarly journals Privacy vs. Reward in Indoor Location-Based Services

2016 ◽  
Vol 2016 (4) ◽  
pp. 102-122 ◽  
Author(s):  
Kassem Fawaz ◽  
Kyu-Han Kim ◽  
Kang G. Shin
Keyword(s):  
Location Privacy ◽  
Indoor Localization ◽  
Service Providers ◽  
Location Based Services ◽  
Location Information ◽  
Privacy Concerns ◽  
Indoor Location ◽  
Location Data ◽  
Personalized Services ◽  
Privacy Risks

AbstractWith the advance of indoor localization technology, indoor location-based services (ILBS) are gaining popularity. They, however, accompany privacy concerns. ILBS providers track the users’ mobility to learn more about their behavior, and then provide them with improved and personalized services. Our survey of 200 individuals highlighted their concerns about this tracking for potential leakage of their personal/private traits, but also showed their willingness to accept reduced tracking for improved service. In this paper, we propose PR-LBS (Privacy vs. Reward for Location-Based Service), a system that addresses these seemingly conflicting requirements by balancing the users’ privacy concerns and the benefits of sharing location information in indoor location tracking environments. PR-LBS relies on a novel location-privacy criterion to quantify the privacy risks pertaining to sharing indoor location information. It also employs a repeated play model to ensure that the received service is proportionate to the privacy risk. We implement and evaluate PR-LBS extensively with various real-world user mobility traces. Results show that PR-LBS has low overhead, protects the users’ privacy, and makes a good tradeoff between the quality of service for the users and the utility of shared location data for service providers.

scholarly journals Complying with Privacy Legislation: From Legal Text to Implementation of Privacy-Aware Location-Based Services

2018 ◽  
Vol 7 (11) ◽  
pp. 442 ◽  
Author(s):  
Mehrnaz Ataei ◽  
Auriol Degbelo ◽  
Christian Kray ◽  
Vitor Santos
Keyword(s):  
Data Privacy ◽  
Location Privacy ◽  
Service Providers ◽  
Location Based Services ◽  
Legal Text ◽  
General Data Protection Regulation ◽  
Location Data ◽  
Privacy Laws ◽  
Technical Issues ◽  
General Data

An individual’s location data is very sensitive geoinformation. While its disclosure is necessary, e.g., to provide location-based services (LBS), it also facilitates deep insights into the lives of LBS users as well as various attacks on these users. Location privacy threats can be mitigated through privacy regulations such as the General Data Protection Regulation (GDPR), which was introduced recently and harmonises data privacy laws across Europe. While the GDPR is meant to protect users’ privacy, the main problem is that it does not provide explicit guidelines for designers and developers about how to build systems that comply with it. In order to bridge this gap, we systematically analysed the legal text, carried out expert interviews, and ran a nine-week-long take-home study with four developers. We particularly focused on user-facing issues, as these have received little attention compared to technical issues. Our main contributions are a list of aspects from the legal text of the GDPR that can be tackled at the user interface level and a set of guidelines on how to realise this. Our results can help service providers, designers and developers of applications dealing with location information from human users to comply with the GDPR.

scholarly journals Enabling Location Privacy Preservation in MANETs Based on Distance, Angle, and Spatial Cloaking

Electronics ◽  
2020 ◽  
Vol 9 (3) ◽  
pp. 458
Author(s):  
Nanlan Jiang ◽  
Sai Yang ◽  
Pingping Xu
Keyword(s):  
Privacy Preservation ◽  
Location Privacy ◽  
Differential Privacy ◽  
Geographic Routing ◽  
Base Station ◽  
Wireless Channel ◽  
Location Based Services ◽  
Location Information ◽  
Communication Overhead ◽  
Spatial Cloaking

Preserving the location privacy of users in Mobile Ad hoc Networks (MANETs) is a significant challenge for location information. Most of the conventional Location Privacy Preservation (LPP) methods protect the privacy of the user while sacrificing the capability of retrieval on the server-side, that is, legitimate devices except the user itself cannot retrieve the location in most cases. On the other hand, applications such as geographic routing and location verification require the retrievability of locations on the access point, the base station, or a trusted server. Besides, with the development of networking technology such as caching technology, it is expected that more and more distributed location-based services will be deployed, which results in the risk of leaking location information in the wireless channel. Therefore, preserving location privacy in wireless channels without losing the retrievability of the real location is essential. In this paper, by focusing on the wireless channel, we propose a novel LPP enabled by distance (ranging result), angle, and the idea of spatial cloaking (DSC-LPP) to preserve location privacy in MANETs. DSC-LPP runs without the trusted third party nor the traditional cryptography tools in the line-of-sight environment, and it is suitable for MANETs such as the Internet of Things, even when the communication and computation capabilities of users are limited. Qualitative evaluation indicates that DSC-LPP can reduce the communication overhead when compared with k-anonymity, and the computation overhead of DSC-LPP is limited when compared with conventional cryptography. Meanwhile, the retrievability of DSC-LPP is higher than that of k-anonymity and differential privacy. Simulation results show that with the proper design of spatial divisions and parameters, other legitimate devices in a MANET can correctly retrieve the location of users with a high probability when adopting DSC-LPP.

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 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 Analysis of Differential Privacy Research in Location and Trajectory Data

2020 ◽  
Author(s):  
Fatima Zahra Errounda ◽  
Yan Liu
Keyword(s):  
Location Privacy ◽  
Differential Privacy ◽  
State Of The Art ◽  
Original Data ◽  
Privacy Preserving ◽  
The State ◽  
Trajectory Data ◽  
Powerful Technique ◽  
Location Data ◽  
Single User

Abstract Location and trajectory data are routinely collected to generate valuable knowledge about users' pattern behavior. However, releasing location data may jeopardize the privacy of the involved individuals. Differential privacy is a powerful technique that prevents an adversary from inferring the presence or absence of an individual in the original data solely based on the observed data. The first challenge in applying differential privacy in location is that a it usually involves a single user. This shifts the adversary's target to the user's locations instead of presence or absence in the original data. The second challenge is that the inherent correlation between location data, due to people's movement regularity and predictability, gives the adversary an advantage in inferring information about individuals. In this paper, we review the differentially private approaches to tackle these challenges. Our goal is to help newcomers to the field to better understand the state-of-the art by providing a research map that highlights the different challenges in designing differentially private frameworks that tackle the characteristics of location data. We find that in protecting an individual's location privacy, the attention of differential privacy mechanisms shifts to preventing the adversary from inferring the original location based on the observed one. Moreover, we find that the privacy-preserving mechanisms make use of the predictability and regularity of users' movements to design and protect the users' privacy in trajectory data. Finally, we explore how well the presented frameworks succeed in protecting users' locations and trajectories against well-known privacy attacks.

scholarly journals Enhanced Internet Mobility and Privacy Using Public Cloud

2017 ◽  
Vol 2017 ◽  
pp. 1-11 ◽  
Author(s):  
Ping Zhang ◽  
Mimoza Durresi ◽  
Arjan Durresi
Keyword(s):  
Location Privacy ◽  
Mobile User ◽  
Mobile Internet ◽  
Cloud Services ◽  
Mobile Users ◽  
Public Cloud ◽  
Network Location ◽  
Location Data ◽  
Internet Users ◽  
Operation Cost

Internet mobile users are concerned more and more about their privacy nowadays as both researches and real world incidents show that leaking of communication and location privacy can lead to serious consequence, and many research works have been done to anonymize individual user from aggregated location data. However, just the communication itself between the mobile users and their peers or website could collect considerable privacy of the mobile users, such as location history, to other parties. In this paper, we investigated the potential privacy risk of mobile Internet users and proposed a scalable system built on top of public cloud services that can hide mobile user’s network location and traffic from communication peers. This system creates a dynamic distributed proxy network for each mobile user to minimize performance overhead and operation cost.

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