scholarly journals pShare: Privacy-Preserving Ride-Sharing System with Minimum-Detouring Route

2022 ◽  
Vol 12 (2) ◽  
pp. 842
Author(s):  
Junxin Huang ◽  
Yuchuan Luo ◽  
Ming Xu ◽  
Bowen Hu ◽  
Jian Long
Keyword(s):  
Service Provider ◽  
Time Estimation ◽  
Privacy Preserving ◽  
Travel Time Estimation ◽  
Sensitive Data ◽  
Transportation Services ◽  
Privacy Leakage ◽  
Ride Sharing ◽  
Multiple Data ◽  
Privacy Problem

Online ride-hailing (ORH) services allow people to enjoy on-demand transportation services through their mobile devices in a short responding time. Despite the great convenience, users need to submit their location information to the ORH service provider, which may incur unexpected privacy problems. In this paper, we mainly study the privacy and utility of the ride-sharing system, which enables multiple riders to share one driver. To solve the privacy problem and reduce the ride-sharing detouring waste, we propose a privacy-preserving ride-sharing system named pShare. To hide users’ precise locations from the service provider, we apply a zone-based travel time estimation approach to privately compute over sensitive data while cloaking each rider’s location in a zone area. To compute the matching results along with the least-detouring route, the service provider first computes the shortest path for each eligible rider combination, then compares the additional traveling time (ATT) of all combinations, and finally selects the combination with minimum ATT. We designed a secure comparing protocol by utilizing the garbled circuit, which enables the ORH server to execute the protocol with a crypto server without privacy leakage. Moreover, we apply the data packing technique, by which multiple data can be packed as one to reduce the communication and computation overhead. Through the theoretical analysis and evaluation results, we prove that pShare is a practical ride-sharing scheme that can find out the sharing riders with minimum ATT in acceptable accuracy while protecting users’ privacy.

scholarly journals Privacy-Preserving Payment Splitting

2020 ◽  
Vol 2020 (2) ◽  
pp. 67-88
Author(s):  
Saba Eskandarian ◽  
Mihai Christodorescu ◽  
Payman Mohassel
Keyword(s):  
Service Provider ◽  
Real Data ◽  
Privacy Preserving ◽  
Excellent Performance ◽  
Sensitive Data ◽  
Transaction Data ◽  
The Core ◽  
Android App ◽  
Financial Transactions ◽  
Gain Access

AbstractWidely used payment splitting apps allow members of a group to keep track of debts between members by sending charges for expenses paid by one member on behalf of others. While offering a great deal of convenience, these apps gain access to sensitive data on users’ financial transactions. In this paper, we present a payment splitting app that hides all transaction data within a group from the service provider, provides privacy protections between users in a group, and provides integrity against malicious users or even a malicious server.The core protocol proceeds in a series of rounds in which users either submit real data or cover traffic, and the server blindly updates balances, informs users of charges, and computes integrity checks on user-submitted data. Our protocol requires no cryptographic operations on the server, and after a group’s initial setup, the only cryptographic tool users need is AES.We implement the payment splitting protocol as an Android app and the accompanying server. We find that, for realistic group sizes, it requires fewer than 50 milliseconds per round of computation on a user’s phone and the server requires fewer than 300 microseconds per round for each group, meaning that our protocol enjoys excellent performance and scalability properties.

A Survey on Privacy-Preserving Outsourced Data on Cloud with Multiple Data Providers

2020 ◽  
Author(s):  
Aman Singh Chauhan ◽  
Dikshika Rani ◽  
Akash Kumar ◽  
Rishabh Gupta ◽  
Ashutosh Kumar Singh
Keyword(s):  
Privacy Preserving ◽  
Multiple Data ◽  
Outsourced Data

Scalable Privacy-preserving Geo-distance Evaluation for Precision Agriculture IoT Systems

2021 ◽  
Vol 17 (4) ◽  
pp. 1-30
Author(s):  
Qiben Yan ◽  
Jianzhi Lou ◽  
Mehmet C. Vuran ◽  
Suat Irmak
Keyword(s):  
Precision Agriculture ◽  
Evaluation System ◽  
Privacy Preserving ◽  
Large Network ◽  
Additional Information ◽  
Modern Agriculture ◽  
Iot Applications ◽  
Privacy Leakage ◽  
Practical Performance ◽  
Real World Datasets

Precision agriculture has become a promising paradigm to transform modern agriculture. The recent revolution in big data and Internet-of-Things (IoT) provides unprecedented benefits including optimizing yield, minimizing environmental impact, and reducing cost. However, the mass collection of farm data in IoT applications raises serious concerns about potential privacy leakage that may harm the farmers’ welfare. In this work, we propose a novel scalable and private geo-distance evaluation system, called SPRIDE, to allow application servers to provide geographic-based services by computing the distances among sensors and farms privately. The servers determine the distances without learning any additional information about their locations. The key idea of SPRIDE is to perform efficient distance measurement and distance comparison on encrypted locations over a sphere by leveraging a homomorphic cryptosystem. To serve a large user base, we further propose SPRIDE+ with novel and practical performance enhancements based on pre-computation of cryptographic elements. Through extensive experiments using real-world datasets, we show SPRIDE+ achieves private distance evaluation on a large network of farms, attaining 3+ times runtime performance improvement over existing techniques. We further show SPRIDE+ can run on resource-constrained mobile devices, which offers a practical solution for privacy-preserving precision agriculture IoT applications.

scholarly journals Federated deep learning for detecting COVID-19 lung abnormalities in CT: a privacy-preserving multinational validation study

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Qi Dou ◽  
Tiffany Y. So ◽  
Meirui Jiang ◽  
Quande Liu ◽  
Varut Vardhanabhuti ◽  
...  
Keyword(s):  
Data Privacy ◽  
Medical Training ◽  
External Validation ◽  
Mainland China ◽  
Privacy Preserving ◽  
Generalization Capability ◽  
Major Focus ◽  
Sensitive Data ◽  
Lung Abnormalities ◽  
Medical Image Diagnosis

AbstractData privacy mechanisms are essential for rapidly scaling medical training databases to capture the heterogeneity of patient data distributions toward robust and generalizable machine learning systems. In the current COVID-19 pandemic, a major focus of artificial intelligence (AI) is interpreting chest CT, which can be readily used in the assessment and management of the disease. This paper demonstrates the feasibility of a federated learning method for detecting COVID-19 related CT abnormalities with external validation on patients from a multinational study. We recruited 132 patients from seven multinational different centers, with three internal hospitals from Hong Kong for training and testing, and four external, independent datasets from Mainland China and Germany, for validating model generalizability. We also conducted case studies on longitudinal scans for automated estimation of lesion burden for hospitalized COVID-19 patients. We explore the federated learning algorithms to develop a privacy-preserving AI model for COVID-19 medical image diagnosis with good generalization capability on unseen multinational datasets. Federated learning could provide an effective mechanism during pandemics to rapidly develop clinically useful AI across institutions and countries overcoming the burden of central aggregation of large amounts of sensitive data.

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