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 Travel Trajectory Frequent Pattern Mining Based on Differential Privacy Protection

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Weiya Wang ◽  
Geng Yang ◽  
Lin Bao ◽  
Ke Ma ◽  
Hao Zhou ◽  
...  
Keyword(s):  
Privacy Protection ◽  
Pattern Mining ◽  
Differential Privacy ◽  
Data Availability ◽  
Frequent Pattern ◽  
Frequent Subgraph Mining ◽  
Trajectory Data ◽  
User Privacy ◽  
Location Data ◽  
Subgraph Mining

Now, many application services based on location data have brought a lot of convenience to people’s daily life. However, publishing location data may divulge individual sensitive information. Because the location records about location data may be discrete in the database, some existing privacy protection schemes are difficult to protect location data in data mining. In this paper, we propose a travel trajectory data record privacy protection scheme (TMDP) based on differential privacy mechanism, which employs the structure of a trajectory graph model on location database and frequent subgraph mining based on weighted graph. Time series is introduced into the location data; the weighted trajectory model is designed to obtain the travel trajectory graph database. We upgrade the mining of location data to the mining of frequent trajectory graphs, which can discover the relationship of location data from the database and protect location data mined. In particular, to improve the identification efficiency of frequent trajectory graphs, we design a weighted trajectory graph support calculation algorithm based on canonical code and subgraph structure. Moreover, to improve the data utility under the premise of protecting user privacy, we propose double processes of adding noises to the subgraph mining process by the Laplace mechanism and selecting final data by the exponential mechanism. Through formal privacy analysis, we prove that our TMDP framework satisfies ε -differential privacy. Compared with the other schemes, the experiments show that the data availability of the proposed scheme is higher and the privacy protection of the scheme is effective.

scholarly journals Research on improved privacy publishing algorithm based on set cover

2019 ◽  
Vol 16 (3) ◽  
pp. 705-731
Author(s):  
Haoze Lv ◽  
Zhaobin Liu ◽  
Zhonglian Hu ◽  
Lihai Nie ◽  
Weijiang Liu ◽  
...  
Keyword(s):  
Privacy Protection ◽  
Data Privacy ◽  
Differential Privacy ◽  
Main Idea ◽  
Data Availability ◽  
Set Cover ◽  
Data Sets ◽  
Data Set ◽  
Query Cover ◽  
Privacy Model

With the invention of big data era, data releasing is becoming a hot topic in database community. Meanwhile, data privacy also raises the attention of users. As far as the privacy protection models that have been proposed, the differential privacy model is widely utilized because of its many advantages over other models. However, for the private releasing of multi-dimensional data sets, the existing algorithms are publishing data usually with low availability. The reason is that the noise in the released data is rapidly grown as the increasing of the dimensions. In view of this issue, we propose algorithms based on regular and irregular marginal tables of frequent item sets to protect privacy and promote availability. The main idea is to reduce the dimension of the data set, and to achieve differential privacy protection with Laplace noise. First, we propose a marginal table cover algorithm based on frequent items by considering the effectiveness of query cover combination, and then obtain a regular marginal table cover set with smaller size but higher data availability. Then, a differential privacy model with irregular marginal table is proposed in the application scenario with low data availability and high cover rate. Next, we obtain the approximate optimal marginal table cover algorithm by our analysis to get the query cover set which satisfies the multi-level query policy constraint. Thus, the balance between privacy protection and data availability is achieved. Finally, extensive experiments have been done on synthetic and real databases, demonstrating that the proposed method preforms better than state-of-the-art methods in most cases.

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 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 An Unequal Image Privacy Protection Method Based on Saliency Detection

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Rongxin Tu ◽  
Wenying Wen ◽  
Changsheng Hua
Keyword(s):  
Privacy Protection ◽  
Saliency Detection ◽  
Image Data ◽  
Feature Integration ◽  
Attention Model ◽  
Data Services ◽  
Privacy Leakage ◽  
Protection Method ◽  
Privacy Issues ◽  
Lightweight Encryption

Cloud platforms provide a good stage for storing and sharing big image data for users, although some privacy issues arise. Image encryption technology can prevent privacy leakage and can ensure secure image data sharing on cloud platforms. Hence, in this paper, an unequal encryption scheme based on saliency detection is proposed. First, based on the mechanism of visual perception and the theory of feature integration, the visual attention model is employed to realize the recognition of significant regions and insignificant regions. Then, a dynamic DNA encryption algorithm is proposed to exploit heavyweight encryption for significant regions, while semi-tensor product compressed sensing is introduced to exploit lightweight encryption and compression for insignificant regions. Experimental results demonstrate that the proposed framework can serve to secure big image data services.

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 Publishing Triangle Counting Histogram in Social Networks Based on Differential Privacy

2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
Tianzi Lv ◽  
Huanzhou Li ◽  
Zhangguo Tang ◽  
Fangzhou Fu ◽  
Jian Cao ◽  
...  
Keyword(s):  
Social Network ◽  
Differential Privacy ◽  
Data Availability ◽  
Projection Algorithm ◽  
Published Data ◽  
Network Data ◽  
Original Graph ◽  
Social Network Data ◽  
Protection Method ◽  
Triangle Counting

The continuous expansion of the number and scale of social networking sites has led to an explosive growth of social network data. Mining and analyzing social network data can bring huge economic value and social benefits, but it will result in privacy leakage and other issues. The research focus of social network data publishing is to publish available data while ensuring privacy. Aiming at the problem of low data availability of social network node triangle counting publishing under differential privacy, this paper proposes a privacy protection method of edge triangle counting. First, an edge-removal projection algorithm TSER based on edge triangle count sorting is proposed to obtain the upper bound of sensitivity. Then, two edge triangle count histogram publishing methods satisfying edge difference privacy are given based on the TSER algorithm. Finally, experimental results show that compared with the existing algorithms, the TSER algorithm can retain more triangles in the original graph, reduce the error between the published data and the original data, and improve the published data availability.

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