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 Achieving Lightweight Verifiable Privacy Preserving Search Over Encrypted Data

2019 ◽  
Vol 3 (3) ◽  
Author(s):  
Selasi Kwame Ocansey ◽  
Charles Fynn Oduro
Keyword(s):  
Service Provider ◽  
Homomorphic Encryption ◽  
Bloom Filter ◽  
Cloud Service ◽  
Privacy Preserving ◽  
Data Sets ◽  
Cloud Service Provider ◽  
Encrypted Data ◽  
Security Proofs

When cloud clients outsource their database to the cloud, they entrust management operations to a cloud service provider who is expected to answer the client’s queries on the cloud where database is located. Efficient techniques can ensure critical requirements for outsourced data’s integrity and authenticity. A lightweight privacy preserving verifiable scheme for outsourcingdatabase securely is proposed, our scheme encrypts data before outsourcing and returned query results are verified with parameters of correctness and completeness. Our scheme is projected on lightweight homomorphic encryption technique and bloom filter which are efficiently authenticated to guarantee the outsourced database’s integrity, authenticity, and confidentiality. An ordering challenge technique is proposed for verifying top-k query results. We conclude by detailing our analysis of security proofs, privacy, verifiability and the performance efficiency of our scheme. Our proposed scheme’s proof and evaluation analysis show its security and efficiency for practical deployment. We also evaluate our scheme’s performances over two UCI data sets.

PRIVACY PRESERVING CLASSIFICATION OVER ENCRYPTED DATA USING FULLY HOMOMORPHIC ENCRYPTION TECHNIQUE

2018 ◽  
Vol 6 (2) ◽  
pp. 36
Author(s):  
MONDAY JUBRIN ABDULLAHI ◽  
ONOMZA WAZIRI VICTOR ◽  
BASHIR ABDULLAHI MUHAMMAD ◽  
ISMAILA IDRIS ◽  
◽  
...  
Keyword(s):  
Homomorphic Encryption ◽  
Privacy Preserving ◽  
Fully Homomorphic Encryption ◽  
Encrypted Data

scholarly journals Scalable Privacy-Preserving Distributed Learning

2021 ◽  
Vol 2021 (2) ◽  
pp. 323-347
Author(s):  
David Froelicher ◽  
Juan R. Troncoso-Pastoriza ◽  
Apostolos Pyrgelis ◽  
Sinem Sav ◽  
Joao Sa Sousa ◽  
...  
Keyword(s):  
Gradient Descent ◽  
Linear Models ◽  
Homomorphic Encryption ◽  
Distributed Learning ◽  
Privacy Preserving ◽  
Encrypted Data ◽  
Multi Level ◽  
Privacy Constraints ◽  
Adversary Model ◽  
High Depth

Abstract In this paper, we address the problem of privacy-preserving distributed learning and the evaluation of machine-learning models by analyzing it in the widespread MapReduce abstraction that we extend with privacy constraints. We design spindle (Scalable Privacy-preservINg Distributed LEarning), the first distributed and privacy-preserving system that covers the complete ML workflow by enabling the execution of a cooperative gradient-descent and the evaluation of the obtained model and by preserving data and model confidentiality in a passive-adversary model with up to N −1 colluding parties. spindle uses multiparty homomorphic encryption to execute parallel high-depth computations on encrypted data without significant overhead. We instantiate spindle for the training and evaluation of generalized linear models on distributed datasets and show that it is able to accurately (on par with non-secure centrally-trained models) and efficiently (due to a multi-level parallelization of the computations) train models that require a high number of iterations on large input data with thousands of features, distributed among hundreds of data providers. For instance, it trains a logistic-regression model on a dataset of one million samples with 32 features distributed among 160 data providers in less than three minutes.

A Location Privacy Preserving Approach on Road Network

2011 ◽  
Vol 34 (5) ◽  
pp. 865-878 ◽  
Author(s):  
Jiao XUE ◽  
Xiang-Yu LIU ◽  
Xiao-Chun YANG ◽  
Bin WANG
Keyword(s):  
Road Network ◽  
Location Privacy ◽  
Privacy Preserving

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.

scholarly journals Privacy Preserving Classification of EEG Data Using Machine Learning and Homomorphic Encryption

2021 ◽  
Vol 11 (16) ◽  
pp. 7360
Author(s):  
Andreea Bianca Popescu ◽  
Ioana Antonia Taca ◽  
Cosmin Ioan Nita ◽  
Anamaria Vizitiu ◽  
Robert Demeter ◽  
...  
Keyword(s):  
Machine Learning ◽  
Data Privacy ◽  
Homomorphic Encryption ◽  
Synthetic Data ◽  
Privacy Preserving ◽  
Supervised Machine Learning ◽  
Computational Time ◽  
Small Integer ◽  
Encrypted Data ◽  
The Impact

Data privacy is a major concern when accessing and processing sensitive medical data. A promising approach among privacy-preserving techniques is homomorphic encryption (HE), which allows for computations to be performed on encrypted data. Currently, HE still faces practical limitations related to high computational complexity, noise accumulation, and sole applicability the at bit or small integer values level. We propose herein an encoding method that enables typical HE schemes to operate on real-valued numbers of arbitrary precision and size. The approach is evaluated on two real-world scenarios relying on EEG signals: seizure detection and prediction of predisposition to alcoholism. A supervised machine learning-based approach is formulated, and training is performed using a direct (non-iterative) fitting method that requires a fixed and deterministic number of steps. Experiments on synthetic data of varying size and complexity are performed to determine the impact on runtime and error accumulation. The computational time for training the models increases but remains manageable, while the inference time remains in the order of milliseconds. The prediction performance of the models operating on encoded and encrypted data is comparable to that of standard models operating on plaintext data.

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.

scholarly journals Enhancing Privacy Preserving Query Retrieval Using Locality Sensitive Hashing

2019 ◽  
pp. 70-73
Author(s):  
Archana M.S. ◽  
K. Deepa
Keyword(s):  
Location Privacy ◽  
Privacy Preserving ◽  
Location Based Services ◽  
Locality Sensitive Hashing ◽  
Range Queries ◽  
Potential Threat ◽  
Encrypted Data ◽  
Points Of Interest ◽  
Spatial Range ◽  
Day By Day

The usage of smart phones is tremendously increasing day by day. Due to this, Location Based Services (LBS) attracted considerably and becomes more popular and vital in the area of mobile applications. On the other hand, the usage of LBS leads to potential threat to user’s location privacy. In this paper, the famous LBS provide information about points of interest (POI) in spatial range query within a given distance. For that, a more efficient and an enhanced privacy-preserving query solution for location based, Efficient Privacy-Location Query (EPLQ) is proposed along with Locality Sensitive Hashing (LSH) reduces the dimensionality of high dimensional data. Experiments are conducted extensively and the results show the efficiency of the proposed algorithm EPLQ in privacy preserving over outsourced encrypted data in spatial range queries. The proposed method performs in spatial range queries and similarity queries of privacy preserving.

An Efficient Privacy-Preserving Rank Query over Encrypted Data in Cloud Computing

2012 ◽  
Vol 35 (11) ◽  
pp. 2215 ◽  
Author(s):  
Fang-Quan CHENG ◽  
Zhi-Yong PENG ◽  
Wei SONG ◽  
Shu-Lin WANG ◽  
Yi-Hui CUI
Keyword(s):  
Cloud Computing ◽  
Privacy Preserving ◽  
Encrypted Data
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