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 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.

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 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 Survey and Experimental Study on Privacy-Preserving Trajectory Data Publishing

2021 ◽  
Author(s):  
Fengmei Jin ◽  
Wen Hua ◽  
Matteo Francia ◽  
Pingfu Chao ◽  
Maria Orlowska ◽  
...  
Keyword(s):  
Privacy Protection ◽  
Traffic Management ◽  
Real Life ◽  
Privacy Preserving ◽  
Location Based Services ◽  
Data Publishing ◽  
Sensitive Information ◽  
Mobility Patterns ◽  
Trajectory Data ◽  
The Right

<div>Trajectory data has become ubiquitous nowadays, which can benefit various real-world applications such as traffic management and location-based services. However, trajectories may disclose highly sensitive information of an individual including mobility patterns, personal profiles and gazetteers, social relationships, etc, making it indispensable to consider privacy protection when releasing trajectory data. Ensuring privacy on trajectories demands more than hiding single locations, since trajectories are intrinsically sparse and high-dimensional, and require to protect multi-scale correlations. To this end, extensive research has been conducted to design effective techniques for privacy-preserving trajectory data publishing. Furthermore, protecting privacy requires carefully balance two metrics: privacy and utility. In other words, it needs to protect as much privacy as possible and meanwhile guarantee the usefulness of the released trajectories for data analysis. In this survey, we provide a comprehensive study and systematic summarization of existing protection models, privacy and utility metrics for trajectories developed in the literature. We also conduct extensive experiments on a real-life public trajectory dataset to evaluate the performance of several representative privacy protection models, demonstrate the trade-off between privacy and utility, and guide the choice of the right privacy model for trajectory publishing given certain privacy and utility desiderata.</div>

scholarly journals Research on Trajectory Data Releasing Method via Differential Privacy Based on Spatial Partition

2018 ◽  
Vol 2018 ◽  
pp. 1-14 ◽  
Author(s):  
Qilong Han ◽  
Zuobin Xiong ◽  
Kejia Zhang
Keyword(s):  
Differential Privacy ◽  
Real Life ◽  
Security And Privacy ◽  
Design Experiment ◽  
Personal Privacy ◽  
Trajectory Data ◽  
Huge Amount ◽  
Spatial Partition ◽  
Modern System ◽  
And Behavior

A number of security and privacy challenges of cyber system are arising due to the rapidly evolving scale and complexity of modern system and networks. The cyber system is a fundamental ingredient for Internet of Things (IoT) and smart city which are driven by huge amount of data. These data carry a lot of information for mining and analysis, especially trajectory data. If unprotected trajectory data is released, it may disclose user’s personal privacy, such as home, religion, and behavior mode, which will endanger their personal security. Until now, many methods for protecting trajectory information have been proposed. However, these methods have the following deficiencies: (i) they cannot defend against speculative attacks if the attacker’s background knowledge is maximized; (ii) when studying the problem, they made some strong assumptions that did not match the reality; (iii) the implementation algorithm is complicated and the time complexity is high, which means that data cannot be executed quickly when the amount is large. So, in this paper, we propose a spatial partition based method to publish trajectory data via differential privacy. First, by exponential mechanism, we divide location set at the same time into different partitions fast and accurately. Then we propose another effective method to release trajectory in a differential private manner. We design experiment based on the real-life dataset and compare it with existing method. The results show that the trajectory dataset released by our algorithm has better usability while ensuring privacy.

scholarly journals A Survey and Experimental Study on Privacy-Preserving Trajectory Data Publishing

2021 ◽  
Author(s):  
Fengmei Jin ◽  
Wen Hua ◽  
Matteo Francia ◽  
Pingfu Chao ◽  
Maria Orlowska ◽  
...  
Keyword(s):  
Privacy Protection ◽  
Traffic Management ◽  
Real Life ◽  
Privacy Preserving ◽  
Location Based Services ◽  
Data Publishing ◽  
Sensitive Information ◽  
Mobility Patterns ◽  
Trajectory Data ◽  
The Right

<div>Trajectory data has become ubiquitous nowadays, which can benefit various real-world applications such as traffic management and location-based services. However, trajectories may disclose highly sensitive information of an individual including mobility patterns, personal profiles and gazetteers, social relationships, etc, making it indispensable to consider privacy protection when releasing trajectory data. Ensuring privacy on trajectories demands more than hiding single locations, since trajectories are intrinsically sparse and high-dimensional, and require to protect multi-scale correlations. To this end, extensive research has been conducted to design effective techniques for privacy-preserving trajectory data publishing. Furthermore, protecting privacy requires carefully balance two metrics: privacy and utility. In other words, it needs to protect as much privacy as possible and meanwhile guarantee the usefulness of the released trajectories for data analysis. In this survey, we provide a comprehensive study and systematic summarization of existing protection models, privacy and utility metrics for trajectories developed in the literature. We also conduct extensive experiments on a real-life public trajectory dataset to evaluate the performance of several representative privacy protection models, demonstrate the trade-off between privacy and utility, and guide the choice of the right privacy model for trajectory publishing given certain privacy and utility desiderata.</div>

COVID-19 Contact Tracing & Privacy Protection, We Are on the Way (Preprint)

2020 ◽  
Author(s):  
Jing Fan
Keyword(s):  
Real World ◽  
Privacy Protection ◽  
Contact Tracing ◽  
Personal Privacy ◽  
The Way

UNSTRUCTURED Smartphone-based contact tracing is proven to be effective in epidemic containment. To maintain its utilization meanwhile ensure the protection of personal privacy, different countries came up with different practices, new exploratory solutions may come into real-world practice soon as well.

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