Social space keyword query based on semantic trajectory

Cao, Keyan; Sun, Qimeng; Liu, Hao‐Li; Liu, Yefan; Meng, Gongjie; Guo, Jingjing

doi:10.1016/j.neucom.2020.02.130

Cited by 10 publications

(7 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In order to test the performance of the location privacy protection method proposed in this paper, the algorithm has been fully experimented in terms of data availability, privacy protection degree, and algorithm running time. The experiment is implemented using Python, and the data sets are Gowalla data set and Geolife data set [18,19]. The experimental environment of this article is PyCharm.…”

Section: Experimental Results and Analysismentioning

confidence: 99%

Differential Privacy Location Protection Method Based on the Markov Model

Wang

Feng

et al. 2021

Wireless Communications and Mobile Computing

View full text Add to dashboard Cite

Location-based services (LBS) have become an important research area with the rapid development of mobile Internet technology, GPS positioning technology, and the widespread application of smart phones and social networks. LBS can provide convenience and flexibility for the users’ daily life, but at the same time, it also brings security risks to the users’ privacy. Untrusted or malicious LBS servers can collect users’ location data through various ways and disclose it to the third party, thus causing users’ privacy leakage. In this paper, a differential privacy location protection method based on the Markov model for user’s location privacy is proposed. Firstly, the transition probability matrix between states of the n -order Markov model is used to predict the occurrence state and development trend of events; thereby, the user’s location is predicted, and then a location prediction algorithm based on the Markov model (LPAM) is proposed. Secondly, a location protection algorithm based on differential privacy (LPADP) is proposed, in which location privacy tree (LPT) is constructed according to the location data and the difficulty of retrieval, the two nodes with the largest predicted value of LPT are allocated with a reasonable privacy budget, and Laplace noise is added to protect location privacy. Theoretical analysis and experimental results show that the proposed method not only meets the requirements of differential privacy and protects location privacy effectively but also has high data availability and low time complexity.

show abstract

Section: Experimental Results and Analysismentioning

confidence: 99%

Differential Privacy Location Protection Method Based on the Markov Model

Wang

Feng

et al. 2021

Wireless Communications and Mobile Computing

View full text Add to dashboard Cite

show abstract

“…This multi-resolution idea is similar to our proposed hierarchical trajectory representation whereas we leverage the semantic hierarchy instead of the regions of interests. Most recently, semantic trajectory analysis has fostered a series of new applications, such as recommender systems in cultural spaces proposed by Angelis et al [26], the itinerary recommender system by Cai et al [27], similar pattern group identification by Cao et al [28], and social-space keyword query by [29]. All of these works are centralized approaches and would not scale up when the huge amount of trajectories across the world need to be compared as that in our problem setting.…”

Section: Related Workmentioning

confidence: 99%

Find Another Me Across the World -- Large-scale Semantic Trajectory Analysis Using Spark

Cai¹,

Lin²

2022

Preprint

View full text Add to dashboard Cite

In today's society, location-based services are widely used which collect a huge amount of human trajectories. Analyzing semantic meanings of these trajectories can benefit numerous real-world applications, such as product advertisement, friend recommendation, and social behavior analysis. However, existing works on semantic trajectories are mostly centralized approaches that are not able to keep up with the rapidly growing trajectory collections. In this paper, we propose a novel large-scale semantic trajectory analysis algorithm in Apache Spark. We design a new hash function along with efficient distributed algorithms that can quickly compute semantic trajectory similarities and identify communities of people with similar behavior across the world. The experimental results show that our approach is more than 30 times faster than centralized approaches without sacrificing any accuracy like other parallel approaches.

show abstract

“…One is the POI dataset [34] from several provinces in China, which contains about 4 million pieces and is stored in the MySQL database. One is the Gowalla dataset [35]. The algorithm proposed in this paper is compared with G-Casper [36] and Casper [37] algorithms in terms of the anonymous success rate, privacy protection degree, algorithm running time, and algorithm antiattack.…”

Section: Experimental Analysismentioning

confidence: 99%

Location Privacy Protection Scheme for LBS in IoT

Xue

et al. 2021

Wireless Communications and Mobile Computing

View full text Add to dashboard Cite

The widespread use of Internet of Things (IoT) technology has promoted location-based service (LBS) applications. Users can enjoy various conveniences brought by LBS by providing location information to LBS. However, it also brings potential privacy threats to location information. Location data that contains private information is often transmitted among IoT networks in LBS, and such privacy information should be protected. In order to solve the problem of location privacy leakage in LBS, a location privacy protection scheme based on k -anonymity is proposed in this paper, in which the Geohash coding model and Voronoi graph are used as grid division principles. We adopt the client-server-to-user (CS2U) model to protect the user’s location data on the client side and the server side, respectively. On the client side, the Geohash algorithm is proposed, which converts the user’s location coordinates into a Geohash code of the corresponding length. On the server side, the Geohash code generated by the user is inserted into the prefix tree, the prefix tree is used to find the nearest neighbors according to the characteristics of the coded similar prefixes, and the Voronoi diagram is used to divide the area units to complete the pruning. Then, using the Geohash coding model and the Voronoi diagram grid division principle, the G-V anonymity algorithm is proposed to find k neighbors in an anonymous area so that the user’s location data meets the k -anonymity requirement in the area unit, thereby achieving anonymity protection of location privacy. Theoretical analysis and experimental results show that our method is effective in terms of privacy and data quality while reducing the time of data anonymity.

show abstract

Social space keyword query based on semantic trajectory

Cited by 10 publications

References 32 publications

Differential Privacy Location Protection Method Based on the Markov Model

Differential Privacy Location Protection Method Based on the Markov Model

Find Another Me Across the World -- Large-scale Semantic Trajectory Analysis Using Spark

Location Privacy Protection Scheme for LBS in IoT

Contact Info

Product

Resources

About