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 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 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 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 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 Towards Region Queries with Strong Location Privacy in Mobile Network

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Songtao Yang ◽  
Qingfeng Jiang
Keyword(s):  
Location Privacy ◽  
New Technologies ◽  
Security Analysis ◽  
Mobile Network ◽  
Sensitive Information ◽  
Location Based Service ◽  
Cryptographic Primitive ◽  
The Common ◽  
Query Semantics ◽  
The Relationship

With the interaction of geographic data and social data, the inference attack has been mounting up, calling for new technologies for privacy protection. Although there are many tangible contributions of spatial-temporal cloaking technologies, traditional technologies are not enough to resist privacy intrusion. Malicious attackers still steal user-sensitive information by analyzing the relationship between location and query semantics. Reacting to many interesting issues, oblivious transfer (OT) protocols are introduced to guarantee location privacy. To our knowledge, OT is a cryptographic primitive between two parties and can be used as a building block for any arbitrary multiparty computation protocol. Armed with previous privacy-preserving technologies, for example, OT, in this work, we first develop a novel region queries framework that can provide robust privacy for location-dependent queries. We then design an OT-assist privacy-aware protocol (or OTPA) for location-based service with rigorous security analysis. In short, the common query of the client in our solution can be divided into two parts, the region query R q and the content query C q , to achieve location k -anonymity, location m -diversity, and query r -diversity, which ensure the privacy of two parties (i.e., client and server). Lastly, we instantiate our OTPA protocol, and experiments show that the proposed OTPA protocol is reasonable and effective.

Protecting User Privacy Better with Query l-Diversity

2010 ◽  
Vol 4 (2) ◽  
pp. 1-18 ◽  
Author(s):  
Fuyu Liu ◽  
Kien A. Hua
Keyword(s):  
Service Provider ◽  
Location Based Services ◽  
Mobile Users ◽  
Simulation Studies ◽  
User Privacy ◽  
Privacy Concerns ◽  
Extensive Simulation ◽  
Novel Concept

This paper examines major privacy concerns in location-based services. Most user privacy techniques are based on cloaking, which achieves location k-anonymity. The key is to reduce location resolution by ensuring that each cloaking area reported to a service provider contains at least k mobile users. However, maintaining location k-anonymity alone is inadequate when the majority of the k mobile users are interested in the same query subject. In this paper, the authors address this problem by defining a novel concept called query l-diversity, which requires diversified queries submitted from the k users. The authors propose two techniques: Expand Cloak and Hilbert Cloak to achieve query l-diversity. To show the effectiveness of the proposed techniques, they compare the improved Interval Cloak technique through extensive simulation studies. The results show that these techniques better protect user privacy.

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.

Privacy-Preserving Spatial Trajectory Prediction Based on a Novel Matrix Representation

2014 ◽  
Vol 5 (1) ◽  
pp. 56-78 ◽  
Author(s):  
Wen-Chen Hu ◽  
Naima Kaabouch ◽  
Hung-Jen Yang ◽  
S. Hossein Mousavinezhad
Keyword(s):  
Traffic Control ◽  
Matrix Representation ◽  
High Mobility ◽  
Location Based Services ◽  
Privacy Concern ◽  
Location Based Service ◽  
User Privacy ◽  
Trajectory Prediction ◽  
Spatial Trajectory ◽  
Human Behavior Recognition

Since the introduction of iPhone in 2007, smartphones have become very popular (e.g., the number of worldwide smartphone sales has surpassed the number of PC sales in 2011). The feature of high mobility and small size of smartphones has created many applications that are not possible or inconvenient for PCs and servers, even laptops. Location-based services (LBS), one of mobile applications, have attracted a great attention recently. This research proposes a location-based service, which predicts a spatial trajectory based on the current and previous trajectories by using a novel matrix representation. Spatial trajectory prediction can be used in a variety of purposes such as travel recommendations and traffic control and planning, but at the same time, just like most location-based services, the user privacy concern is a major issue. Without rigorous privacy protection, users would be reluctant to use the service. The proposed method is simple but effective and user privacy is rigorously preserved at the same time because the trajectory prediction is performed at the user-side. Additionally, this research is not only useful but also pedagogical because it involves a variety of topics like (i) mobile computing, (ii) mobile security, and (iii) human behavior recognition.

scholarly journals A Trajectory Privacy Preserving Scheme in the CANNQ Service for IoT

Sensors ◽  
2019 ◽  
Vol 19 (9) ◽  
pp. 2190 ◽  
Author(s):  
Lin Zhang ◽  
Chao Jin ◽  
Hai-ping Huang ◽  
Xiong Fu ◽  
Ru-chuan Wang
Keyword(s):  
Nearest Neighbor ◽  
Security Analysis ◽  
Response Speed ◽  
Privacy Preserving ◽  
Location Based Services ◽  
Continuous Queries ◽  
Communication Overhead ◽  
Nearest Neighbor Query ◽  
Aggregate Nearest Neighbor ◽  
Query Algorithm

Nowadays, anyone carrying a mobile device can enjoy the various location-based services provided by the Internet of Things (IoT). ‘Aggregate nearest neighbor query’ is a new type of location-based query which asks the question, ‘what is the best location for a given group of people to gather?’ There are numerous, promising applications for this type of query, but it needs to be done in a secure and private way. Therefore, a trajectory privacy-preserving scheme, based on a trusted anonymous server (TAS) is proposed. Specifically, in the snapshot queries, the TAS generates a group request that satisfies the spatial K-anonymity for the group of users—to prevent the location-based service provider (LSP) from an inference attack—and in continuous queries, the TAS determines whether the group request needs to be resent by detecting whether the users will leave their secure areas, so as to reduce the probability that the LSP reconstructs the users’ real trajectories. Furthermore, an aggregate nearest neighbor query algorithm based on strategy optimization, is adopted, to minimize the overhead of the LSP. The response speed of the results is improved by narrowing the search scope of the points of interest (POIs) and speeding up the prune of the non-nearest neighbors. The security analysis and simulation results demonstrated that our proposed scheme could protect the users’ location and trajectory privacy, and the response speed and communication overhead of the service, were superior to other peer algorithms, both in the snapshot and continuous queries.

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