A Location Privacy-Preserving Protocol Based on Homomorphic Encryption and Key Agreement

2013 ◽  
Author(s):  
Xiaoling Zhu ◽  
Yang Lu ◽  
Xiaojuan Zhu ◽  
Shuwei Qiu
Keyword(s):  
Key Agreement ◽  
Location Privacy ◽  
Homomorphic Encryption ◽  
Privacy Preserving

scholarly journals Hail the Closest Driver on Roads: Privacy-Preserving Ride Matching in Online Ride Hailing Services

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Haining Yu ◽  
Hongli Zhang ◽  
Xiangzhan Yu
Keyword(s):  
Service Provider ◽  
Road Network ◽  
Location Privacy ◽  
Homomorphic Encryption ◽  
Privacy Preserving ◽  
Smartphone App ◽  
Network Embedding ◽  
Encrypted Data ◽  
Short Notice ◽  
Road Distance

Online ride hailing (ORH) services enable a rider to request a driver to take him wherever he wants through a smartphone app on short notice. To use ORH services, users have to submit their ride information to the ORH service provider to make ride matching, such as pick-up/drop-off location. However, the submission of ride information may lead to the leakages of users’ privacy. In this paper, we focus on the issue of protecting the location information of both riders and drivers during ride matching and propose a privacy-preserving online ride matching scheme, called pRMatch. It enables an ORH service provider to find the closest available driver for an incoming rider over a city-scale road network, while protecting the location privacy of both riders and drivers against the ORH service provider and other unauthorized participants. In pRMatch, we compute the shortest road distance over encrypted data by using road network embedding and partially homomorphic encryption and further efficiently compare encrypted distances by using ciphertext packing and shuffling. The theoretical analysis and experimental results demonstrate that pRMatch is accurate and efficient, yet preserving users’ location privacy.

scholarly journals Privacy Preserving in LBS resilient to Location Injection Attacks

2019 ◽  
Vol 8 (2S11) ◽  
pp. 3621-3625
Keyword(s):  
Location Privacy ◽  
Homomorphic Encryption ◽  
Privacy Preserving ◽  
Location Based Services ◽  
Injection Attacks ◽  
Point Of Interest ◽  
The Right ◽  
User Location

Location-based services have become indispensable in people's life with expeditious development of technology. Location-based services(LBS) refers to the services provided by the LBS servers with regards to area and point of interest. Alternatively, the LBS means getting the right information at the right place in time. Protecting user location privacy is the most challenging factor in LBS. This survey aims to present various mechanisms in preserving the user's location privacy and proposes a mechanism for preserving the privacy of user location and query against the location injection attacks. We will be discussing credibility based k- anonymity mechanism for preserving the location of the user and homomorphic encryption for preserving the query of the user resilient location injection attacks in this paper.

A Privacy Preserving Scheme for Vehicle to Grid Networks Based on Homomorphic Cryptography

2014 ◽  
Vol 1014 ◽  
pp. 516-519
Author(s):  
Zhong Wei Sun ◽  
Wen Xiao Yan
Keyword(s):  
Location Privacy ◽  
Homomorphic Encryption ◽  
Privacy Preserving ◽  
Close Monitoring ◽  
Fully Homomorphic Encryption ◽  
Privacy Concerns ◽  
Vehicle To Grid ◽  
Grid Networks ◽  
The Status ◽  
Communication Scheme

Vehicle–to-Grid (V2G) is an essential component of smart grid for their capability of providing better ancillary services. The operation is based on monitoring the status of individual Electric Vehicle (EV) continuously and designing an incentive scheme to attract sufficient participating EVs. However, the close monitoring might raise privacy concerns from the EV owners about real identity and location leakage. Based on the fully homomorphic encryption algorithm, a privacy preserving V2G communication scheme is put forward in the paper. The proposed protocol can achieve the identity and location privacy, security requirement of confidentiality and integrity of the communications.

Location Privacy-Preserving Task Recommendation with Geometric Range Query in Mobile Crowdsensing

2021 ◽  
pp. 1-1
Author(s):  
Chuan Zhang ◽  
Liehuang Zhu ◽  
Chang Xu ◽  
Jianbing Ni ◽  
Cheng Huang ◽  
...  
Keyword(s):  
Location Privacy ◽  
Range Query ◽  
Privacy Preserving ◽  
Mobile Crowdsensing

scholarly journals Privacy Preserving Machine Learning with Homomorphic Encryption and Federated Learning

2021 ◽  
Vol 13 (4) ◽  
pp. 94
Author(s):  
Haokun Fang ◽  
Quan Qian
Keyword(s):  
Machine Learning ◽  
Homomorphic Encryption ◽  
Privacy Preserving ◽  
Great Success ◽  
Learning Framework ◽  
Computational Overhead ◽  
Important Concern ◽  
Speed Up ◽  
Key Length ◽  
Core Idea

Privacy protection has been an important concern with the great success of machine learning. In this paper, it proposes a multi-party privacy preserving machine learning framework, named PFMLP, based on partially homomorphic encryption and federated learning. The core idea is all learning parties just transmitting the encrypted gradients by homomorphic encryption. From experiments, the model trained by PFMLP has almost the same accuracy, and the deviation is less than 1%. Considering the computational overhead of homomorphic encryption, we use an improved Paillier algorithm which can speed up the training by 25–28%. Moreover, comparisons on encryption key length, the learning network structure, number of learning clients, etc. are also discussed in detail in the paper.

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