User's Private Information Research Articles

Multi-keyword Cipher-text Retrieval Method for Smart Grid Edge Computing*

With the continuous deepening of China's power grid intelligent upgrading and the rise of edge computing technology, power grid terminals can carry out data storage and intelligent calculation at the edge of the power grid. However, the generated data by users may cause the leakage of user privacy information. The power grid edge system can retrieve the user's electricity data on demand, so as to improve the level of privacy protection and reduce the demand of transmission and computing resources through the localized storage of data. Therefore, how to carry out privacy protection and on-demand retrieval is a key problem to realize the intelligent power grid edge. In this paper, a multi-keyword cipher-text retrieval scheme (MCR- SGEC) for smart grid edge computing is proposed, which can achieve accurate matching of index and trap door, and has low spatial complexity. At the same time, on this basis, a multi-keyword fuzzy cipher-text retrieval method for smart grid edge computing (MFSE-SGEC) is proposed, which allows users to have spelling errors, which makes the user experience better.

Truthful online double auction based dynamic resource provisioning for multi-objective trade-offs in IaaS clouds.

Auction designs have recently been adopted for static and dynamic resource provisioning in IaaS clouds, such as Microsoft Azure and Amazon EC2. However, the existing mechanisms are mostly restricted to simple auctions, single-objective, offline setting, one-sided interactions either among cloud users or cloud service providers (CSPs), and possible misreports of cloud user’s private information. This paper proposes a more realistic scenario of online auctioning for IaaS clouds, with the unique characteristics of elasticity for time-varying arrival of cloud user requests under the time-based server maintenance in cloud data centers. We propose an online truthful double auction technique for balancing the multi-objective trade-offs between energy, revenue, and performance in IaaS clouds, consisting of a weighted bipartite matching based winning-bid determination algorithm for resource allocation and a Vickrey–Clarke–Groves (VCG) driven algorithm for payment calculation of winning bids. Through rigorous theoretical analysis and extensive trace-driven simulation studies exploiting Google cluster workload traces, we demonstrate that our mechanism significantly improves the performance while promising truthfulness, heterogeneity, economic efficiency, individual rationality, and has a polynomial-time computational complexity.

A Privacy‐Preserving Blockchain Supervision Framework in the Multiparty Setting

Data supervision is an effective method to ensure the legality of user data on blockchain. However, the massive growth of data makes it difficult to achieve data supervision in existing blockchain applications. Also, data supervision often leads to problems such as disclosure of transaction data and user privacy information. To address these issues, this paper proposes a privacy‐preserving blockchain supervision system (BSS) in the multiparty setting, where a supervision chain is introduced to realize data supervision on blockchain. All sensitive information such as user information in the supervising data is encrypted by the attribute‐based encryption (ABE) technology, so that both privacy protection and access control on user data can be achieved. Theoretical analysis and comparison show that the proposed BSS scheme is efficient, and experimental analysis indicates the practicality of our BSS scheme.

Application of QR Code Technology in the Design of User Information Privacy Protection Logistics System

This paper takes the protection of user privacy information in express logistics industry as the main research object, and designs a scheme to protect user privacy by using RFID technology and multi-layer encryption technology. The scheme can achieve the dual privacy protection for the internal and external of the logistics company, and ensure that the person responsible for the disclosure of privacy information can be reviewed.

Semantic and Trade-Off Aware Location Privacy Protection in Road Networks Via Improved Multi-Objective Particle Swarm Optimization

Location privacy protection is an essential but challenging topic in the field of network security. Although the existing research methods, such as k-anonymity, mix zone, and differential privacy, show significant success, they usually neglect the location semantic and the proper trade-off between privacy and utility, which may allow attackers to obtain user privacy information by revealing the semantic correlation between the anonymous region and user's real location, thus causing privacy leakage. To solve this problem, we propose a location privacy protection scheme based on the k-anonymity technique, which provides practical location privacy-preserving through generating an anonymous set. This paper proposes a new location privacy attack strategy termed semantic relativity attack (SRA), which considers the location semantic problem. Correspondingly, a semantic and trade-off aware location privacy protection mechanism (STA-LPPM) is presented to achieve privacy protection with both high-level privacy and utility. To be specific, we model the location privacy protection as a multi-objective optimization problem and propose the Improved Multi-Objective Particle Swarm Optimization (IMOPSO) to generate the optimal anonymous set calculating the well-design fitness functions of the multi-objective optimization problem. In this way, the privacy scheme can provide mobile users with the right balance of privacy protection and service quality. Experiments reveal that our privacy scheme can effectively resist the semantic relativity attack while preventing significant utility degrading.

BlockEV: Efficient and Secure Charging Station Selection for Electric Vehicles

The Intelligent Transportation System (ITS) has become essential for the economical and technological development of a country. The maturity of communication technologies (Vehicle to Infrastructure (V2I) and Vehicle to Vehicle (V2V)) and the amalgamation of smart grids, electric vehicles (EVs) and energy trading resulted in a storm of research opportunities for green ITS. In addition, the combination of vehicular communication technologies and ITS enable efficient selection of EV charging stations (CS) and scheduling EVs charging requirements in real-time. However, the untrusted centralized nature of energy markets and EV charging infrastructures result in several privacy and security threats to EV user's private information. These security and privacy threats include targeted advertisements, privacy leakage, selling data to third party, etc. In this work, we propose BlockEV, a blockchain-based efficient CS selection protocol for EVs to ensure the security and privacy of the EV users, availability of the reserved time slots at CSs, high Quality of Service (QoS) and enhanced EV user comfort. First, a blockchain-based framework is introduced to implement secure charging services and trusted reservation for EVs with the execution of smart contract. Second, we focus on the efficient CS selection and propose a mechanism for EVs to select the CS locally without sharing private information to CS, while fulfilling their service requirements. Evaluations show that the proposed BlockEV is scalable with significantly low blockchain transaction and storage overhead.

Fine-Grained Webpage Fingerprinting Using Only Packet Length Information of Encrypted Traffic

Encrypted web traffic can reveal sensitive information of users, such as their browsing behaviors. Existing studies on encrypted traffic analysis focus on website fingerprinting. We claim that fine-grained webpage fingerprinting, which speculates specific webpages on a same website visited by a victim, allows exploiting more user private information, e.g., shopping interests in an online shopping mall. Since webpages from the same website usually have very similar traffic traces that make them indistinguishable, existing solutions may end up with low accuracy. In this paper, we propose FineWP , a novel fine-grained webpage fingerprinting method. We make an observation that the length information of packets in bidirectional client-server interactions can be distinctive features for webpage fingerprinting. The extracted features are then fed into traditional machine learning models to train classifiers, which achieve both high accuracy and low training overhead. We collect two real-world traffic datasets and construct closed- and open-world evaluations to verify the effectiveness of FineWP. The experimental results demonstrate that FineWP is superior to the state-of-the-art methods in terms of accuracy, time complexity and stability.

Two Factor Authentication by Using SMS for Web Based Application

Two factor authentications had two entry levels for authentication’s mechanism. This study had used Short Messaging Service (SMS) technology as a second factor in doing authentication on the web application. It can minimize the problems that occur regarding to the illegal access over the user privacy information on the Internet. The system that uses two factor authentications is capable to give the priority and safety in aspect of user’s privacy information from the web based applications

An extended intelligent water drop approach for efficient VM allocation in secure cloud computing framework

Cloud computing provides many advantages services to its users like online resources accessibility, better cost management, dynamic resource pooling, and efficient Virtual Machine (VM) allocation. With so many useful services, cloud computing also introduces several security risks of cloud user information privacy as well as select the minimum number of VM to execute task load and improve resource utilization are some challenging issues. The objective of this work is to achieve higher resource utilization rate while considering the security of the cloud data. We extended the natural phenomena based Intelligent Water Drop (IWD) algorithm and proposed a VM allocation algorithm which optimizes the task execution in a secure cloud environment. The proposed work is implemented on the CloudSim simulation toolkit, and to test the validity of the algorithm, a comparison is performed with other well-known VM allocation policies in the cloud computing. The experimental simulation results showed that the proposed VM allocation policy performed better than exiting VM allocation approaches.

ARM-Embedded Implementation of a Novel Color Image Encryption and Transmission System Based on Optical Chaos

Cloud servers are very suitable for sharing, and storing image data, and meanwhile it is confronted with many security, and privacy problems. Optical chaos, and hardware implementation of image encryption possess distinct superiorities in secret image sharing. Hence, combining the advantages of advanced reduced instruction set computer machine (ARM)-embedded platform Exynos4412 model, together with vertical-cavity surface emitting laser (VCSEL) subject to positive optoelectronic feedback (POEF), a novel color image encryption, and transmission system is experimentally realized, and appraised. We investigate the dynamic behaviors of the VCSEL, and obtain the chaotic output. An analog to digital conversion (ADC) module is utilized for optical chaos data acquisition. The improved gravity model, and the double sine map are exploited to encrypt the image, and the encrypted image is securely shared by cloud services. The user can download the encrypted image, and decrypt it by the hardware board. The experiment results reveal that the system has an excellent resistance to common attacks. Compared to the numerical simulation, our hardware implementation for such a color image encryption, and transmission system is formidable to actualize, but we successfully implemented, and tested in a real-world environment. By comparing experimental, and simulation results, the consistent results are found. Therefore, in the case of similar encryption effect, the user's information privacy protection in the actual industrial or commercial systems can be securely guaranteed on our hardware platform.

Lightning-fast and privacy-preserving outsourced computation in the cloud

In this paper, we propose a framework for lightning-fast privacy-preserving outsourced computation framework in the cloud, which we refer to as LightCom. Using LightCom, a user can securely achieve the outsource data storage and fast, secure data processing in a single cloud server different from the existing multi-server outsourced computation model. Specifically, we first present a general secure computation framework for LightCom under the cloud server equipped with multiple Trusted Processing Units (TPUs), which face the side-channel attack. Under the LightCom, we design two specified fast processing toolkits, which allow the user to achieve the commonly-used secure integer computation and secure floating-point computation against the side-channel information leakage of TPUs, respectively. Furthermore, our LightCom can also guarantee access pattern protection during the data processing and achieve private user information retrieve after the computation. We prove that the proposed LightCom can successfully achieve the goal of single cloud outsourced data processing to avoid the extra computation server and trusted computation server, and demonstrate the utility and the efficiency of LightCom using simulations.

Blockchain Enabled Named Data Networking for Secure Vehicle-to-Everything Communications

A huge amount of information is expected to be exchanged in vehicular networks through vehicle-to-everything (V2X) communications for enhancing overall traffic efficiency and road safety. However, there are several critical challenges to be addressed before completely realizing the full potential of V2X networking. Privacy-aware security is one of the central components to be addressed for V2X communications. This paper presents a novel framework by leveraging the best features of two emerging technologies: blockchain technology and named data networking (NDN) for privacy-aware secure V2X communications. The proposed framework does not use the private information of users (owners, drivers, pedestrians, passengers, cyclists, etc.) in vehicular networks while providing verifiable secure V2X communications by using non-private information such as number plate of the vehicle (like in ParkMobile App or E-ZPass systems use) for integrity and accountability of the communications. Specifically, integrity and accountability in the proposed framework for its users are achieved by amalgamating the best features of blockchain technology and NDN. Furthermore, the proposed approach aims to increase the trust and transparency and reduce the business friction in smart transplantation systems.

User Privacy Protection Behavior and Information Sharing in Mobile Health Application

User Privacy Protection Behavior and Information Sharing in Mobile Health Application

Learning in the Air: Secure Federated Learning for UAV-Assisted Crowdsensing

Unmanned aerial vehicles (UAVs) combined with artificial intelligence (AI) have opened a revolutionized way for mobile crowdsensing (MCS). Conventional AI models, built on aggregation of UAVs’ sensing data (typically contain private and sensitive user information), may arise severe privacy and data misuse concerns. Federated learning, as a promising distributed AI paradigm, has opened up possibilities for UAVs to collaboratively train a shared global model without revealing their local sensing data. However, there still exist potential security and privacy threats for UAV-assisted crowdsensing with federated learning due to vulnerability of central curator, unreliable contribution recording, and low-quality shared local models. In this paper, we propose SFAC, a s ecure f ederated learning framework for U A V-assisted M C S. Specifically, we first introduce a blockchain-based collaborative learning architecture for UAVs to securely exchange local model updates and verify contributions without the central curator. Then, by applying local differential privacy, we design a privacy-preserving algorithm to protect UAVs’ privacy of updated local models with desirable learning accuracy. Furthermore, a two-tier reinforcement learning-based incentive mechanism is exploited to promote UAVs’ high-quality model sharing when explicit knowledge of network parameters are not available in practice. Extensive simulations are conducted, and the results demonstrate that the proposed SFAC can effectively improve utilities for UAVs, promote high-quality model sharing, and ensure privacy protection in federated learning, compared with existing schemes.

Research on Medical Big Data Application Model Based on Data Separation

Data attributed data, privacy attributed privacy. At present, big data development now going all walks of life, in order to better carry out medical research and so on, the medical industry select development of healthcare big data, but the data are still some big problems in terms of security: user privacy information disclosure and other. This paper proposes a new way of thinking: data separation storage method, by the user's information data into information data block medical data and personal information data blocks, try a way to solve the existing problem of private data leakage, and study the practical application ability of medical big data based on this data separation storage method.

A Detailed Security Assessment of the EV Charging Ecosystem

The drive for efficient, reliable, green, and connected smart cities has promoted the use of electric vehicles (EVs) as the main future means of transportation. This resulted in a breakthrough in the anticipated number of adopted EVs by the year 2020, and consequently an urge for an available and trustworthy EV charging infrastructure. The diversity of the involved players, the used technologies, the bulk data exchange, and the widespread nature of the charging network give rise to security concerns in the form of message tampering, spoofing, or delaying among others to disconcert the charging service along with the underlying power layer. Furthermore, confidentiality and privacy of user information (i.e. identity, location, payment information, etc.) is another major concern associated with the deployment and use of the charging infrastructure. Thus, there is a need to identify and classify such concerns, and devise suitable solutions for a secure charging infrastructure. In this paper, we present a security assessment of the EV charging infrastructure. We highlight and categorize cyber threats targeting different players in a charging system, along with the security solutions presented in the literature. Finally, we present a gap analysis and insights into future research directions for EV charging system security.

Research on User’ Continuous Usage of Online Healthcare Services From the Perspective of Affect Appeal

Based on the extended ECM-ISC continuous usage model and integration of the CAC model, a model that illustrates users’ continuous usage of online healthcare services is developed from the perspective of affect appeal by introducing satisfaction and worry variables. The factors influencing users’ continuous usage of online healthcare services in China are closely investigated in the paper. Based on the results of E-mail and Wen Juan Xing website online survey, the empirical analyses of the continuous usage of online healthcare services in China are conducted, utilizing SPSS and AMO. Through the screening of the questionnaires, 521 of the 600 questionnaires are valid. The main results of this study are as follows. Service quality, expectation confirmation degree, privacy concern, satisfaction degree, and continuous use intention are positively correlated with expectation confirmation degree, satisfaction degree, concern, and continuous use intention and continuous use behavior, respectively, while concern and continuous use intention are negatively correlated. It is found in this paper that both satisfaction and concern have a direct impact on users’ continuous use intention and continuous use behavior, and the effect of concern becomes increasingly prominent. Besides, service quality and privacy concern are the core variables affecting satisfaction degree and concern. Based on the above research results, this paper suggests to strengthen the security of user privacy information, and meanwhile, improve the quality of online doctors, which can promote users’continuous use intention and continuous use behavior, and thus further improve the efficiency of the use of health services online and quality medical resource allocation, as well as reduce hospital time and economic cost.

Amplified locality‐sensitive hashing‐based recommender systems with privacy protection

SummaryWith the advent of Internet of Things (IoT) age, the variety and volume of web services have been increasing at a fast speed. This often leads to users' selections for web services more complicated. Under the circumstance, a variety of methods such as collaborative filtering are adopted to deal with this challenging situation. While traditional collaborative filtering method has some shortcomings, one of which is that only centralized user‐service data are considered while distributed quality data from multiple platform are ignored. Generally, service recommendation across different platforms often involves data communication among multiple platforms, during which user privacy may be disclosed and much computational time is required. Considering these challenges, a unique amplified locality‐sensitive hashing (LSH)‐based service recommendation method, that is, SRAmplified‐LSH, is proposed in the article. SRAmplified‐LSH can guarantee a good balance between accuracy and efficiency of recommendation and user privacy information. Finally, extensive experiments deployed on WS‐DREAM dataset validate the feasibility of our proposed method.

Active Defense by Mimic Association Transmission in Edge Computing

A large amount of real-time data, including user privacy information, control commands, and other sensitive data, are transmitted in edge computing networks. It requires high-speed and reliable data transmission in dynamic edge computing networks. Traditional methods with passive defense cannot cope with the covert and complicated attacks. Edge computing networks require active defense during data transmission. Existing active defense methods based on dynamic network ignore the connectivity and link quality reduced by attacks and do not adjust defense positively. To maximize the defense revenue in moving adjustment strategy, this paper proposes the model of active defense for edge computing network data interaction. In this model, the network topology mimic association protocol is designed to associate multi-paths and multi-parameters automatically. On one hand, considering the transmission reliability and defensive revenue reduction caused by dynamic network transformation, a real-time multi-feature anomaly detection algorithm based on Non-extensive entropy and Renyi cross entropy is proposed. Based on this, a moving communication path alliance can be constructed pseudo-randomly. On the other hand, this paper proposes a Hidden Markov based state prediction model and a mimic transformation strategy for The Network Topology Mimic Association Graph based on predicted states. Combining these two ways improves the data transmission service quality of the active defense technology in edge computing networks. Experiments are carried in simulated power networks. The results show that our method outperforms the popular methods in terms of transmission efficiency, reliability, and anti-attack performance.

Cognitive fog for health: a distributed solution for smart city

The internet of things (IoT) connects numerous physical devices in urban areas to implement smart cities, and health monitoring has emerged as the most promising application area in such cities. However, the current health monitoring solution heavily relies on cloud-based data centres to integrate data, which put the city's confidential data and user's private information at risk of leakage. Fog computing as an extension of cloud computing has attracted lots of attention in the IoT community, for its safety to local data and friendliness to time-sensitive applications. This article adopts the paradigm of fog computing and proposes the cognitive fog for health (CFH) to address the requirement of estimating urban-level health impact in the real-world scenario.