Dynamic Whitelisting Using Locality Sensitive Hashing

2018 ◽  
pp. 181-185
Author(s):  
Jayson Pryde ◽  
Nestle Angeles ◽  
Sheryl Kareen Carinan
Keyword(s):  
Locality Sensitive Hashing

Subspace k-anonymity algorithm for location-privacy preservation based on locality-sensitive hashing

2019 ◽  
Vol 23 (5) ◽  
pp. 1167-1185
Author(s):  
Xiaohan Wang ◽  
Yonglong Luo ◽  
Shiyang Liu ◽  
Taochun Wang ◽  
Huihui Han
Keyword(s):  
Privacy Preservation ◽  
Location Privacy ◽  
Locality Sensitive Hashing

scholarly journals Trusted Player Transfer Evaluation for Sport Markets based on Blockchain and Locality-Sensitive Hashing

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Chao Liu ◽  
Zengxi Li ◽  
Shunshun Liu ◽  
Jushi Xie ◽  
Chao Yan ◽  
...  
Keyword(s):  
Locality Sensitive Hashing

scholarly journals Privacy-Aware Multidimensional Mobile Service Quality Prediction and Recommendation in Distributed Fog Environment

2018 ◽  
Vol 2018 ◽  
pp. 1-8 ◽  
Author(s):  
Wenwen Gong ◽  
Lianyong Qi ◽  
Yanwei Xu
Keyword(s):  
Service Selection ◽  
Locality Sensitive Hashing ◽  
Mobile Service ◽  
Service Recommendation ◽  
User Privacy ◽  
Service Usage ◽  
Selection Decisions ◽  
Wide Range ◽  
Cross Platform ◽  
Computational Resources

With the ever-increasing popularity of mobile computing technology, a wide range of computational resources or services (e.g., movies, food, and places of interest) are migrating to the mobile infrastructure or devices (e.g., mobile phones, PDA, and smart watches), imposing heavy burdens on the service selection decisions of users. In this situation, service recommendation has become one of the promising ways to alleviate such burdens. In general, the service usage data used to make service recommendation are produced by various mobile devices and collected by distributed edge platforms, which leads to potential leakage of user privacy during the subsequent cross-platform data collaboration and service recommendation process. Locality-Sensitive Hashing (LSH) technique has recently been introduced to realize the privacy-preserving distributed service recommendation. However, existing LSH-based recommendation approaches often consider only one quality dimension of services, without considering the multidimensional recommendation scenarios that are more complex but more common. In view of this drawback, we improve the traditional LSH and put forward a novel LSH-based service recommendation approach named SerRecmulti-qos, to protect users’ privacy over multiple quality dimensions during the distributed mobile service recommendation process.

A Bayesian Perspective on Locality Sensitive Hashing with Extensions for Kernel Methods

2015 ◽  
Vol 10 (2) ◽  
pp. 1-32 ◽  
Author(s):  
Aniket Chakrabarti ◽  
Venu Satuluri ◽  
Atreya Srivathsan ◽  
Srinivasan Parthasarathy
Keyword(s):  
Kernel Methods ◽  
Locality Sensitive Hashing
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