scholarly journals A Trajectory Privacy Protection Method Based on Random Sampling Differential Privacy

2021 ◽  
Vol 10 (7) ◽  
pp. 454
Author(s):  
Tinghuai Ma ◽  
Fagen Song
Keyword(s):  
Random Sampling ◽  
Privacy Protection ◽  
Differential Privacy ◽  
Traffic Monitoring ◽  
City Management ◽  
Trajectory Data ◽  
Privacy Concerns ◽  
Protection Method ◽  
Real World Datasets ◽  
Parameter Values

With the popularity of location-aware devices (e.g., smart phones), a large number of trajectory data were collected. The trajectory dataset can be used in many fields including traffic monitoring, market analysis, city management, etc. The collection and release of trajectory data will raise serious privacy concerns for users. If users’ privacy is not protected enough, they will refuse to share their trajectory data. In this paper, a new trajectory privacy protection method based on random sampling differential privacy (TPRSDP), which can provide more security protection, is proposed. Compared with other methods, it takes less time to run this method. Experiments are conducted on two real world datasets to validate the proposed scheme, and the results are compared with others in terms of running time and information loss. The performance of the scheme with different parameter values is verified. The setting of the new scheme parameters is discussed in detail, and some valuable suggestions are given.

scholarly journals A trajectory data publishing algorithm satisfying local suppression

2021 ◽  
Vol 17 (2) ◽  
pp. 155014772199340
Author(s):  
Xiaohui Li ◽  
Yuliang Bai ◽  
Yajun Wang ◽  
Bo Li
Keyword(s):  
Privacy Protection ◽  
Loss Rate ◽  
Differential Privacy ◽  
Classification Tree ◽  
Data Availability ◽  
Trajectory Data ◽  
User Privacy ◽  
Data Set ◽  
Privacy Leakage ◽  
Protection Method

Suppressing the trajectory data to be released can effectively reduce the risk of user privacy leakage. However, the global suppression of the data set to meet the traditional privacy model method reduces the availability of trajectory data. Therefore, we propose a trajectory data differential privacy protection algorithm based on local suppression Trajectory privacy protection based on local suppression (TPLS) to provide the user with the ability and flexibility of protecting data through local suppression. The main contributions of this article include as follows: (1) introducing privacy protection method in trajectory data release, (2) performing effective local suppression judgment on the points in the minimum violation sequence of the trajectory data set, and (3) proposing a differential privacy protection algorithm based on local suppression. In the algorithm, we achieve the purpose Maximal frequent sequence (MFS) sequence loss rate in the trajectory data set by effective local inhibition judgment and updating the minimum violation sequence set, and then establish a classification tree and add noise to the leaf nodes to improve the security of the data to be published. Simulation results show that the proposed algorithm is effective, which can reduce the data loss rate and improve data availability while reducing the risk of user privacy leakage.

scholarly journals Preserving Differential Privacy for Similarity Measurement in Smart Environments

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Kok-Seng Wong ◽  
Myung Ho Kim
Keyword(s):  
Privacy Protection ◽  
Differential Privacy ◽  
Smart Environments ◽  
Coefficient Function ◽  
Sensitive Information ◽  
Smart Environment ◽  
Privacy Concerns ◽  
Privacy Model ◽  
The Subject ◽  
Measurement Metric

Advances in both sensor technologies and network infrastructures have encouraged the development of smart environments to enhance people’s life and living styles. However, collecting and storing user’s data in the smart environments pose severe privacy concerns because these data may contain sensitive information about the subject. Hence, privacy protection is now an emerging issue that we need to consider especially when data sharing is essential for analysis purpose. In this paper, we consider the case where two agents in the smart environment want to measure the similarity of their collected or stored data. We use similarity coefficient functionFSCas the measurement metric for the comparison with differential privacy model. Unlike the existing solutions, our protocol can facilitate more than one request to computeFSCwithout modifying the protocol. Our solution ensures privacy protection for both the inputs and the computedFSCresults.

scholarly journals Constructing elastic distinguishability metrics for location privacy

2015 ◽  
Vol 2015 (2) ◽  
pp. 156-170 ◽  
Author(s):  
Konstantinos Chatzikokolakis ◽  
Catuscia Palamidessi ◽  
Marco Stronati
Keyword(s):  
Location Privacy ◽  
Differential Privacy ◽  
City Center ◽  
Privacy Concerns ◽  
Extensive Evaluation ◽  
Elastic Mechanism ◽  
Real World Datasets ◽  
Location Based Social Networks ◽  
The City ◽  
Geometrical Distance

Abstract With the increasing popularity of hand-held devices, location-based applications and services have access to accurate and real-time location information, raising serious privacy concerns for their users. The recently introduced notion of geo-indistinguishability tries to address this problem by adapting the well-known concept of differential privacy to the area of location-based systems. Although geo-indistinguishability presents various appealing aspects, it has the problem of treating space in a uniform way, imposing the addition of the same amount of noise everywhere on the map. In this paper we propose a novel elastic distinguishability metric that warps the geometrical distance, capturing the different degrees of density of each area. As a consequence, the obtained mechanism adapts the level of noise while achieving the same degree of privacy everywhere. We also show how such an elastic metric can easily incorporate the concept of a “geographic fence” that is commonly employed to protect the highly recurrent locations of a user, such as his home or work. We perform an extensive evaluation of our technique by building an elastic metric for Paris’ wide metropolitan area, using semantic information from the OpenStreetMap database. We compare the resulting mechanism against the Planar Laplace mechanism satisfying standard geo-indistinguishability, using two real-world datasets from the Gowalla and Brightkite location-based social networks. The results show that the elastic mechanism adapts well to the semantics of each area, adjusting the noise as we move outside the city center, hence offering better overall privacy.1

scholarly journals Non-Global Privacy Protection Facing Sensitive Areas in Face Images

2021 ◽  
Vol 38 (6) ◽  
pp. 1677-1687
Author(s):  
Chao Liu ◽  
Jing Yang ◽  
Yining Zhang ◽  
Xuan Zhang ◽  
Weinan Zhao ◽  
...  
Keyword(s):  
Privacy Protection ◽  
Differential Privacy ◽  
Total Error ◽  
Regional Growth ◽  
Sensitive Area ◽  
Image Size ◽  
Personal Privacy ◽  
Sensitive Areas ◽  
Face Images ◽  
Protection Method

Face images, as an information carrier, are naturally weak in privacy. If they are collected and analyzed by malicious third parties, personal privacy will leak, and many other unmeasurable losses will occur. Differential privacy protection of face images is mainly being studied under non-interactive frameworks. However, the ε-effect impacts the entire image under these frameworks. Besides, the noise influence is uniform across the protected image, during the realization of the Laplace mechanism. The differential privacy of face images under interactive mechanisms can protect the privacy of different areas to different degrees, but the total error is still constrained by the image size. To solve the problem, this paper proposes a non-global privacy protection method for sensitive areas in face images, known as differential privacy of landmark positioning (DPLP). The proposed algorithm is realized as follows: Firstly, the active shape model (ASM) algorithm was adopted to position the area of each face landmark. If the landmark overlaps a subgraph of the original image, then the subgraph would be taken as a sensitive area. Then, the sensitive area was treated as the seed for regional growth, following the fusion similarity measurement mechanism (FSMM). In our method, the privacy budget is only allocated to the seed; whether any other insensitive area would be protected depends on whether the area exists in a growing region. In addition, when a subgraph meets the criterion for merging with multiple seeds, the most reasonable seed to be merged would be selected by the exponential mechanism. Experimental results show that the DPLP algorithm satisfies ε-differential privacy, its total error does not change with image size, and the noisy image remains highly available.

Personalized Trajectory Privacy Protection Method Based on User-Requirement

2018 ◽  
Vol 27 (03) ◽  
pp. 1850006 ◽  
Author(s):  
Zhaowei Hu ◽  
Jing Yang ◽  
Jianpei Zhang
Keyword(s):  
Privacy Protection ◽  
Real Life ◽  
Manhattan Distance ◽  
Protective Effects ◽  
Personal Privacy ◽  
Trajectory Data ◽  
Data Utilization ◽  
Time Intervals ◽  
User Requirement ◽  
Protection Method

Trajectory data often provides useful information that can be utilized in real-life applications, such as traffic planning and location-based advertising. Because people’s trajectory information can result in serious personal privacy leakage, trajectory privacy protection methods are employed. However, existing methods assume and use the same privacy requirements for all trajectories, which affect privacy protection efficiency and data utilization. This paper proposes a trajectory privacy protection method based on user requirement. By dividing different time intervals, it sets different privacy protection parameters for different trajectories to provide more detailed privacy protection. The proposed method utilizes the divided time intervals and privacy protection requirements to form a privacy requirement matrix, to construct an anonymous trajectory equivalence class and undirected graph. Then, trajectories are processed to form anonymous sets. Euclidean distance is also replaced with Manhattan distance in calculating the distance of the trajectories, which would improve the privacy protection and data utility and narrow the gap between the theoretical privacy protection and the actual protective effects. Comparative experiments demonstrate that the proposed method outperforms other similar methods in regards to both privacy protection and data utilization.

scholarly journals Location recommendation privacy protection method based on location sensitivity division

2019 ◽  
Vol 2019 (1) ◽  
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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