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A new trend of using deep reinforcement learning for traffic signal control has become a spotlight in the Intelligent Transportation System (ITS). However, the traditional intelligent traffic signal control system always collects and transmits vehicle information (e.g., vehicle location, speed, etc.) in the form of plaintext, which would result in the leakage of commuters’ privacy and thus bring unnecessary troubles to users. In this paper, we propose a privacy-preserving traffic signal control for an intelligent transportation system (PrivacySignal). It relies on the existing road facilities to achieve the privacy of commuters, which guarantees the practicality of the system. Real-time decision-making and confidentiality of the system can be achieved simultaneously via the design of a series of secure and efficient interactive protocols, that are based on additive secret sharing, to perform the deep $Q$ -network (DQN). Moreover, the security of PrivacySignal is testified, meanwhile, the system effectiveness, and the overall efficiency of PrivacySignal is demonstrated through theoretical analysis and simulation experiments. Compared with the existing privacy-preserving schemes of the intelligent traffic signal, PrivacySignal provides a general DQN based privacy-preserving traffic signal control strategy architecture with high efficiency and low-performance loss. [ABSTRACT FROM AUTHOR]

In this paper, we propose a lightweight privacy-preserving Faster R-CNN framework (SecRCNN) for object detection in medical images. Faster R-CNN is one of the most outstanding deep learning models for object detection. Using SecRCNN, healthcare centers can efficiently complete privacy-preserving computations of Faster R-CNN via the additive secret sharing technique and edge computing. To implement SecRCNN, we design a series of interactive protocols to perform the three stages of Faster R-CNN, namely feature map extraction, region proposal and regression and classification. To improve the efficiency of SecRCNN, we improve the existing secure computation sub-protocols involved in SecRCNN, including division, exponentiation and logarithm. The newly proposed sub-protocols can dramatically reduce the number of messages exchanged during the iterative approximation process based on the coordinate rotation digital computer algorithm. Moreover, the effectiveness, efficiency and security of SecRCNN are demonstrated through comprehensive theoretical analysis and extensive experiments. The experimental findings show that the communication overhead in computing division, logarithm and exponentiation decreases to 36.19%, 73.82% and 43.37%, respectively. [ABSTRACT FROM AUTHOR]

Sketches, as a kind of probabilistic data structures, have been considered as the most promising solution for network measurement in recent years. Most sketches do not work well for skewed network traffic. To address this problem, we propose a family of sketch frameworks, namely the Pyramid family. The first member of our Pyramid family is the S-Pyramid framework, which includes two techniques: counter-pair sharing for high accuracy, and word acceleration for fast speed. The second member of our Pyramid family is the Mini-Pyramid framework, which projects the S-Pyramid framework into one counter, bringing more flexibility in application while keeping the accuracy. To demonstrate the generality of our Pyramid family, we apply both frameworks to sketches of CM, CU, Count, and Augmented. To demonstrate the flexibility of the Mini-Pyramid framework, we further apply Mini-Pyramid to SBF and the On-Off sketch. The experimental results show that, the S-Pyramid framework can reduce the ARE by up to 7.12 times compared with the original sketches, while improving the throughput by up to 2.37 times; the Mini-Pyramid framework can reduce the ARE by up to 29.2 times, at the cost of 21.3% lower throughput on average. [ABSTRACT FROM AUTHOR]

With the rapid development of Internet of Vehicles (IoV), vehicle-based spatial crowdsourcing (SC) applications have been proposed and widely applied to various fields. However, location privacy leakage is a serious issue in spatial crowdsourcing because workers who participate in a crowdsourcing task are required to upload their driving locations. In this paper, we propose a decentralized location privacy-preserving SC for IoV, which allows vehicle users to securely participate in SC with ensuring the task’s location policy privacy and providing multi-level privacy preservation for workers’ locations. Specifically, we introduce blockchain technology into SC, which can eliminate the control of vehicle user data by SC-server. We combine the additively homomorphic encryption and circle-based location verification to ensure the confidentiality of task’s location policy. To achieve multi-level privacy preservation for workers’ driving locations, we only reveal a grid where workers are located in. The size of the grid represents the level of privacy preservation. We leverage the order-preserving encryption and non-interactive zero-knowledge proof to prevent workers from illegally obtaining rewards by forging their driving locations. The security analysis results show that our framework can satisfy the above requirements. In addition, the experiment results demonstrate that our framework is efficient and feasible in practice. [ABSTRACT FROM AUTHOR]

With the development of Industry 4.0, the communication of smart grid has recently been taken seriously to ensure secure communication between operator and control center. However, the authentication process between them faces many challenges. Once the attacker successfully authenticated in the control center, the privacy data in the smart grid may leak and cause irreparable damage to the user. In addition, operator authentication is one of the most basic and crucial processes. Therefore, we propose the eye-movement and iris recognition based authentication (EmIr-Auth), a novel biometrics-based remote operator authentication scheme. EmIr-Auth uses the recorded eye-movement trajectory and randomly selected iris image to authenticate operators, which is beneficial in that it is able to get rid of many cryptographic computations, as well as the need to minimize message exchange. Furthermore, except for a high-resolution camera, we do not require any additional biometric sensors in this scheme. Using the Burrows–Abadi–Needham logic, in this article, we demonstrate that our scheme provides secure authentication. Moreover, we analyze the attacks that EmIr-Auth can resist by informal security analysis. Experimental results show that EmIr-Auth is efficient enough to deploy on portable devices and reduce the overhead of authentication procedure. [ABSTRACT FROM AUTHOR]

Spectrum auction is deemed as an efficient approach for redistributing spectrum and realizing dynamic spectrum access. Though there have been extensive research endeavors in designing spectrum auctions that are truthful with desirable social welfare, security and privacy concerns give rise to new challenges and calls for the design of privacy- preserving spectrum auction mechanisms. In this article, we begin with an overview of conventional spectrum auction mechanisms, then discuss the potential security threats in them. In particular, bidders' private information, for example, bid values and geo-locations, are submitted to the non-trustworthy auctioneer and can be observed by rival bidders who may leverage this information to rig the auction. To address this problem, we outline the framework for privacy-preserving auction mechanism design, which applies cryptographic tools to the original auction and ensures that the private information of bidders is protected, while the basic functionalities of the auction are maintained. Based on the proposed framework, we further construct a privacy-preserving heterogeneous spectrum double auction, which is shown to uphold the closely similar allocation efficiency with the original auction, and realizes privacy preservation with moderate communication and computation overheads. [ABSTRACT FROM AUTHOR]

Intelligent connected vehicle trajectory data are of great value for data mining applications such as traffic management and commercial institutions. However, the leakage of sensitive trajectory makes the user hesitate to use the system if no privacy-preserving mechanism is adopted. In this paper, we propose a privacy-preserving mechanism with differential privacy called RPTR, which protects a vehicle's real-time trajectory data release. First, RPTR adopts a dynamic sampling method to process the trajectory data to meet the application load and practicability. Meanwhile, to ensure the data availability, ensemble Kalman filter based on users’ position transfer probability matrix is used in the prediction calculation. Also, we construct the privacy budget allocation method based on regional privacy weight to provide better protection for regions with high user density. Through our analysis and experiments, RPTR not only protects the privacy of real-time trajectory data but also guarantees the data availability. [ABSTRACT FROM AUTHOR]