Location Privacy Preservation and Location-based Service Quality Tradeoff Framework Based on Differential Privacy

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.

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Location Privacy Preservation Mechanism for Location-Based Service With Incomplete Location Data

IEEE Access ◽

10.1109/access.2020.2995504 ◽

2020 ◽

Vol 8 ◽

pp. 95843-95854

Author(s):

Xudong Yang ◽

Ling Gao ◽

Jie Zheng ◽

Wei Wei

Keyword(s):

Privacy Preservation ◽

Location Privacy ◽

Location Based Service ◽

Location Data

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KLPPS: A k-Anonymous Location Privacy Protection Scheme via Dummies and Stackelberg Game

Security and Communication Networks ◽

10.1155/2021/9635411 ◽

2021 ◽

Vol 2021 ◽

pp. 1-15

Author(s):

Dongdong Yang ◽

Baopeng Ye ◽

Wenyin Zhang ◽

Huiyu Zhou ◽

Xiaobin Qian

Keyword(s):

Service Quality ◽

Privacy Protection ◽

Privacy Preservation ◽

Location Privacy ◽

Stackelberg Game ◽

Single Point ◽

Third Party ◽

Protection Scheme ◽

Inference Attacks ◽

Location Privacy Protection

Protecting location privacy has become an irreversible trend; some problems also come such as system structures adopted by location privacy protection schemes suffer from single point of failure or the mobile device performance bottlenecks, and these schemes cannot resist single-point attacks and inference attacks and achieve a tradeoff between privacy level and service quality. To solve these problems, we propose a k-anonymous location privacy protection scheme via dummies and Stackelberg game. First, we analyze the merits and drawbacks of the existing location privacy preservation system architecture and propose a semitrusted third party-based location privacy preservation architecture. Next, taking into account both location semantic diversity, physical dispersion, and query probability, etc., we design a dummy location selection algorithm based on location semantics and physical distance, which can protect users’ privacy against single-point attack. And then, we propose a location anonymous optimization method based on Stackelberg game to improve the algorithm. Specifically, we formalize the mutual optimization of user-adversary objectives by using the framework of Stackelberg game to find an optimal dummy location set. The optimal dummy location set can resist single-point attacks and inference attacks while effectively balancing service quality and location privacy. Finally, we provide exhaustive simulation evaluation for the proposed scheme compared with existing schemes in multiple aspects, and the results show that the proposed scheme can effectively resist the single-point attack and inference attack while balancing the service quality and location privacy.

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Staircase based differential privacy with branching mechanism for location privacy preservation in wireless sensor networks

Computers & Security ◽

10.1016/j.cose.2018.03.002 ◽

2018 ◽

Vol 77 ◽

pp. 36-48 ◽

Cited By ~ 6

Author(s):

Bodhi Chakraborty ◽

Shekhar Verma ◽

Krishna Pratap Singh

Keyword(s):

Wireless Sensor Networks ◽

Sensor Networks ◽

Privacy Preservation ◽

Location Privacy ◽

Differential Privacy ◽

Wireless Sensor

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Location recommendation privacy protection method based on location sensitivity division

EURASIP Journal on Wireless Communications and Networking ◽

10.1186/s13638-019-1606-y ◽

2019 ◽

Vol 2019 (1) ◽

Cited By ~ 4

Author(s):

Chunyong Yin ◽

Xiaokang Ju ◽

Zhichao Yin ◽

Jin Wang

Keyword(s):

Service Quality ◽

Privacy Protection ◽

Location Privacy ◽

Differential Privacy ◽

Experimental Results ◽

Location Information ◽

Location Recommendation ◽

Protection Method ◽

Privacy Budget ◽

The Impact

AbstractLocation-based recommendation services can provide users with convenient services, but this requires monitoring and collecting a large amount of location information. In order to prevent location information from being leaked after monitoring and collection, location privacy must be effectively protected. Therefore, this paper proposes a privacy protection method based on location sensitivity for location recommendation. This method uses location trajectories and check-in frequencies to set a threshold so as to classify location sensitivity levels. The corresponding privacy budget is then assigned based on the sensitivity to add Laplace noise that satisfies the differential privacy. Experimental results show that this method can effectively protect the user’s location privacy and reduce the impact of differential privacy noise on service quality.

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

Advances in Mechanical Engineering ◽

10.1177/1687814018822393 ◽

2019 ◽

Vol 11 (1) ◽

pp. 168781401882239 ◽

Cited By ~ 1

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.

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