Guarantee User Privacy Research Articles

Multikeyword-Ranked Search Scheme Supporting Extreme Environments for Internet of Vehicles

In recent years, the cloud infrastructure has been developed as a promising sharing system for the Internet of Vehicles (IoV) communication. During the information exchange, search service over ciphertext called searchable encryption (SE) is an extraordinary method to prevent data breaches. However, two open problems still need to be solved for ranked search, which hinders the application practicality in IoV. First, each data owner must store the extra information to distribute weight values dynamically. Second, ranking in the cloud has not been supported by most existing schemes. In this paper, to address the above problems and fit the characteristics of real-time data exchange in IoV, we present a multi-keyword ranked search scheme supporting extreme environments for the IoV. Specifically, our system designs a unique encrypted index tree structure to realize the multi-keyword ranked retrieval, the weight value dynamic adaptive calculation, and dynamic updating in IoV. Moreover, we use a master-slave dual server model to cope with extreme environments and propose a “Greedy Breadth-First Search” algorithm to achieve an effective sub-linear search. Finally, comprehensive security analysis and experimental simulation for the proposed system prove that our system can guarantee user privacy and acceptable efficiency.

PDPChain: A Consortium Blockchain-Based Privacy Protection Scheme for Personal Data

With the advances and innovations in digital technologies, blockchain has empowered advancements in communications and networking, promising to build trust and establish secure decentralized communications networks. Unfortunately, current personal data privacy protection schemes still suffer from explicit storage, lack of data ownership and implementation of fine-grained access control by users, and lack of transparency and auditability of data. In this article, we propose a personal data privacy protection scheme based on consortium blockchain that stores original data encrypted with an improved Paillier homomorphic encryption mechanism, namely PDPChain, where users realize fine-grained access control based on ciphertext policy attribute-based encryption (CP-ABE) on blockchain. In this scheme, consortium blockchain combines distributed private clusters to store the encrypted data, improving data transmission efficiency, and guaranteeing user privacy and security through off-chain storage and on-chain transmission synergy. In addition, it is more lightweight encryption and demarcation, ultimately protecting personal data privacy and providing a secure and trusted way to obtain information for data mining. For the performance testing, data in the form of files are used as an example, and the scheme is designed and simulated on Hyperledger Fabric and InterPlanetary File System. Experimental results show that the improved Paillier encryption mechanism reduces the overall encryption and decryption elapsed time by 25% and encryption elapsed time by 48%. Furthermore, the proposed CP-ABE access control method is adaptive to storing and sharing a massive amount of data. With the increase in the number of access control policies, the overall time-consuming of the scheme does not increase, and the time-consuming of decryption can also be stabilized at about 2 s.

Hybrid Model for Sentiment Analysis of Bitcoin Prices using Deep Learning Algorithm

Bitcoin is a decentralized digital currency, which is not controlled by a single authority or government. Bitcoin uses blockchain technology to verify transactions and guarantee user security and privacy. The fluctuating value of bitcoin is influenced by opinions that develop because many people use these opinions as a basis for buying or selling bitcoins. Knowledge to find out the market conditions of bitcoin based on public opinion is very necessary. This study aims to develop a hybrid model for bitcoin sentiment analysis. The dataset used came from comments on the Indodax website chat room, as many as 2890 data were successfully collected, then do data preprocessing, translate to english, text labeling and used hybrid parallel CNN and LSTM using word embedding glove 100 dimensions. Results of the experiments conducted, at 90:10 data splitting and 100 epochs is the best model with 88% accuracy, 86% precision, 78% recall and 81% f1-score, while the classification of opinion text comments on indodax chat results in 64.22% neutral comments, 21.14% positive comments and 14.63% negative comments. Based on research results, use of a parallel hybrid model provides a high accuracy value in classifying text, from these results positive comments are more than negative so that investors are advised to buy bitcoins.

Design of three-factor secure and efficient authentication and key-sharing protocol for IoT devices

The remarkable advance of the Internet of Things (IoT) has smoothed the way to the interconnection of various mobile devices in secure access and communication. In order to guarantee user privacy and anonymity in public networks, a large number of mutual authentication and key-sharing protocols between different IoT devices and multi-servers have been proposed. Due to resource-constrained and inefficient IoT devices, most previous protocols can confront assorted malicious attacks, such as eavesdropping, counterfeiting, chip cloning, device forgery, and other attacks. These attacks may be exposed the user’s private key or other sensitive data. To solve these problems, physical unclonable function (PUF) is a lightweight security primitive that utilizes random process deviations that cannot be controlled during chip manufacturing to generate device-unique digital signatures. In this paper, we combined the key-sharing scheme based on PUF on the hardware side, which solves relevant security problems such as device cloning and key tampering. Furthermore, we propose a three-factor secure and efficient authentication and key-sharing protocol, leveraging the inherent security properties of passwords, biometrics, and PUFs. We demonstrate the security of our proposed protocol based on computational Bilinear Diffie–Hellman Problem (BDH) and k-CAA hard problems and the Proverif tool. Compared with existing relevant protocols, our protocol meets various security properties and defends against varied security threats. The low computational cost, communication overhead, and device storage indicate that our protocol is applicable to resource-constrained IoT devices.

Peer-to-peer energy trading and smart contracting platform of community-based virtual power plant

Traditional centralized transactions require a control center for user demand matching, settlement and other processes. However, with the increase in the penetration rate of distributed energy in the community, the explosive increase in the number of transactions leads to a decrease in efficiency and it is difficult to guarantee user privacy and information security. The smart contract technology based on blockchain technology has the characteristics of decentralization, traceability and tamper resistance, and these key factors show unique advantages in distributed energy transactions. This paper explores Ethereum and smart contract technology, designs a peer-to-peer energy sharing mechanism with reward and punishment incentives and establishes a smart contract trading platform for smart community-based virtual power plant (CVPP). This paper verifies the functionality and effectiveness of smart contract. The results show that when the supply and demand ratio changes, the user can conduct energy transactions according to the contract without a third-party organization, which solves the problem of trust between the two parties and achieves the expected effect and runs successfully. In addition, the simulation results show that the peer-to-peer transaction based on smart contracts reduces the energy cost per household and increases the total benefit of CVPP.

Fairness-Aware Federated Learning With Unreliable Links in Resource-Constrained Internet of Things

In order to make full use of the network data and guarantee user privacy simultaneously, federated learning (FL) is proposed to enable distributed intelligence for local nodes without sharing data with each other. However, in practice, due to resource limitations, traditional FL suffers from node scheduling and parameter transmission failure, which not only affects the final performance but also further reduces the fairness of the participating nodes. This article addresses the challenge and proposes an FL method to enhance the performance of FL on the basis of guaranteeing the fairness of the local nodes in a resource-constrained Internet of Things (IoT) network. Specifically, an analytical model is first constructed to characterize the performance of FL with joint considerations of node fairness, unreliable parameter transmissions as well as resource limitations. Thereafter, a statistically reweighted aggregation (SRA) scheme is proposed for parameter aggregation and the corresponding model is proved to be unbiased to that based on ideal parameter transmissions. With the knowledge of time dependency of the global model, we further extend SRA and propose a reliable SRA (RSRA) scheme. Additionally, we prove RSRA is able to achieve higher stability performance than SRA in model training. Furthermore, the convergence bound of the proposed RSRA is derived analytically, based on which an adaptive local training scheme is proposed under a given resource budget. Finally, extensive experiments are carried out with a public data set to validate the effectiveness of the proposed scheme with comparisons of other baseline schemes.

Bedtime Monitoring for Fall Detection and Prevention in Older Adults.

Life expectancy has increased, so the number of people in need of intensive care and attention is also growing. Falls are a major problem for older adult health, mainly because of the consequences they entail. Falls are indeed the second leading cause of unintentional death in the world. The impact on privacy, the cost, low performance, or the need to wear uncomfortable devices are the main causes for the lack of widespread solutions for fall detection and prevention. This work present a solution focused on bedtime that addresses all these causes. Bed exit is one of the most critical moments, especially when the person suffers from a cognitive impairment or has mobility problems. For this reason, this work proposes a system that monitors the position in bed in order to identify risk situations as soon as possible. This system is also combined with an automatic fall detection system. Both systems work together, in real time, offering a comprehensive solution to automatic fall detection and prevention, which is low cost and guarantees user privacy. The proposed system was experimentally validated with young adults. Results show that falls can be detected, in real time, with an accuracy of 93.51%, sensitivity of 92.04% and specificity of 95.45%. Furthermore, risk situations, such as transiting from lying on the bed to sitting on the bed side, are recognized with a 96.60% accuracy, and those where the user exits the bed are recognized with a 100% accuracy.

A blockchain-based infection tracing and notification system by non-fungible tokens

SARS-CoV2 pandemic is heavily affecting our lives. Many actions have been undertaken to slow down its expansion and, among the others, contact tracing applications are the less invasive to monitor the spread of the virus. The idea behind contact tracing is to track contacts between people by the exchange of identifiers, not linked to individuals, exploiting the use of Bluetooth Low Energy (BLE) technology to estimate the duration and proximity of contacts. The data collected in this way is used for the sole purpose of notifying a potential contact with an infected person without revealing their identity and location. This paper presents a contact tracing protocol based on blockchain technology that exploits smart contracts for reporting contacts at risk of contagion. The novelty of the proposed solution is the use of Non Fungible Tokens (NFT) to guarantee user privacy through a decentralized approach, equipped with a reliable non-proprietary notification mechanism that allows public access to anonymous infections data.

Design of a secure and privacy preserving authentication protocol for telecare medical information systems

AbstractWith the advancement of communication technology, Telecare Medicine Information Systems (TMIS) provides convenient healthcare services for patients, doctors, and health organizations over the insecure Internet. As a result, when accessing sensitive medical data over an insecure connection, user privacy, data security, and user authentication is very crucial. A secure authentication protocol plays a crucial role in securing communications over TMIS, these environments are very vulnerable to numerous attacks due to resource constraints and the nature of the communication medium. Recently, an efficient authentication framework has been introduced for TMIS to address various security issues. However, the existing mutual authentication and key agreement protocols are vulnerable to replay attacks, insider attacks, impersonation attacks, and password guessing attacks. Furthermore, the current authentication systems do not guarantee user privacy and the fair key agreement between the patient and the medical server. We propose a more robust authentication approach for healthcare information systems to address these security issues. To assess the authentication protocol's security strength, we employ formal verification tools like Automated Validation of Internet Security Protocols and Applications (AVISPA). Finally, a thorough performance analysis demonstrates that the proposed mutual authentication framework not only ensures privacy but also maintains a low computing overhead. As a result, when compared to other related systems, the proposed authentication protocol is both secure and computationally efficient.

A blockchain‐based mobile crowdsensing scheme with enhanced privacy

AbstractWith the popularity and development of sensors‐containing intelligent terminals, mobile crowdsensing system (MCS) based on the Internet of Things (IoT) has become a new paradigm of application. By the MCS, the pervasive smart device users are enabled to collect large‐scale data cost‐effectively, for crowd intelligent extraction and human‐centric service delivery. However, most of the existing MCSs are based on a centralized structure vulnerable to attacks and intrusions. Moreover, the data collected through crowdsensing are diverse and difficult to guarantee user privacy, especially during the payment and data upload stages. In this article, we propose a blockchain‐based privacy‐preserving crowdsensing (BPPC) scheme based on the distributed structure, to protect user privacy. First, we combine the multiblockchain technology and K‐anonymity to construct anonymity groups for the confusion. Second, we present the random algorithm HashProof to select candidates from the anonymity groups to avoid deployment of Trusted Third Party (TTP) or agent server. Ultimately, we design encryption‐based algorithms building trust and authentication mechanisms in the system to guarantee the confidentiality of user data and achieve the accurate distribution of rewards. To verify the effectiveness and efficiency of the BPPC scheme, extensive experiments were conducted.

A scalable private Bitcoin payment channel network with privacy guarantees

While Bitcoin heavily dominates the cryptocurrency markets, its use in micropayments is still a challenge due to long transaction confirmation times and high fees. Recently, the concept of off-chain transactions is introduced that led to the idea of establishing a payment channel network called Lightning Network (LN), which utilizes multi-hop payments. Off-chain links provide the ability to make instant payments without a need to writing to Blockchain. However, LN's design still favors fees, and it is creating hub nodes or relays that defeat the purpose of Blockchain. In addition, it is still not reliable, as not all transactions are guaranteed to be delivered to their destinations. These issues hinder its wide adoption by retailers. To address this issue, in this paper, we argue that the retailers could create a private payment channel network among them to serve their business needs, just like the concept of private Blockchains. The goal is to build a pure peer-to-peer topology that will prevent the formation of hub nodes while also eliminating the need for any relays to increase the robustness of the payments. Assuming off-chain links as edges and retailers as nodes, we formulate the problem as a multi-flow commodity problem where transactions represent the commodities from various sources to destinations. As the multi-flow commodity problem is NP-Complete, we propose a heuristic approach that utilizes Dijkstra's shortest path algorithm for payments in a dynamic way by updating the edge weights when new paths need to be found. The order of transactions is randomized to provide fairness among the retailers. We further extend this approach to guarantee the privacy of payments by forcing all the payments to travel at least three hops. We utilized k-shortest path algorithm to choose from k options that will meet our criterion. The evaluations indicate that the proposed heuristic comes close to an optimal solution while providing scalability and guaranteeing user privacy.

Security and Privacy of QR Code Applications: A Comprehensive Study, General Guidelines and Solutions

The widespread use of smartphones is boosting the market take-up of dedicated applications and among them, barcode scanning applications. Several barcodes scanners are available but show security and privacy weaknesses. In this paper, we provide a comprehensive security and privacy analysis of 100 barcode scanner applications. According to our analysis, there are some apps that provide security services including checking URLs and adopting cryptographic solutions, and other apps that guarantee user privacy by supporting least privilege permission lists. However, there are also apps that deceive the users by providing security and privacy protections that are weaker than what is claimed. We analyzed 100 barcode scanner applications and we categorized them based on the real security features they provide, or on their popularity. From the analysis, we extracted a set of recommendations that developers should follow in order to build usable, secure and privacy-friendly barcode scanning applications. Based on them, we also implemented BarSec Droid, a proof of concept Android application for barcode scanning. We then conducted a user experience test on our app and we compared it with DroidLa, the most popular/secure QR code reader app. The results show that our app has nice features, such as ease of use, provides security trust, is effective and efficient.

Turbo-Aggregate: Breaking the Quadratic Aggregation Barrier in Secure Federated Learning

Federated learning is a distributed framework for training machine learning models over the data residing at mobile devices, while protecting the privacy of individual users. A major bottleneck in scaling federated learning to a large number of users is the overhead of secure model aggregation across many users. In particular, the overhead of the state-of-the-art protocols for secure model aggregation grows quadratically with the number of users. In this article, we propose the first secure aggregation framework, named Turbo-Aggregate, that in a network with N users achieves a secure aggregation overhead of O(NlogN), as opposed to O(N <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ), while tolerating up to a user dropout rate of 50%. Turbo-Aggregate employs a multi-group circular strategy for efficient model aggregation, and leverages additive secret sharing and novel coding techniques for injecting aggregation redundancy in order to handle user dropouts while guaranteeing user privacy. We experimentally demonstrate that Turbo-Aggregate achieves a total running time that grows almost linear in the number of users, and provides up to 40× speedup over the state-of-the-art protocols with up to N=200 users. Our experiments also demonstrate the impact of model size and bandwidth on the performance of Turbo-Aggregate.

LAKS-NVT: Provably Secure and Lightweight Authentication and Key Agreement Scheme Without Verification Table in Medical Internet of Things

Wireless body area networks (WBANs) and wireless sensor networks (WSNs) are important concepts for the Internet of Things (IoT). They have been applied to various healthcare services to ensure that users can access convenient medical services by exchanging physiological data between user and medical server. User physiological data is collected by sensor nodes and sent to medical service providers, doctors, etc. using public channels. However, these channels are vulnerable to various potential attacks, and hence, it is essential to design provably secure and lightweight mutual authentication (MA) schemes for medical IoT to protect user privacy and achieve secure communication. A lightweight mutual authentication and key agreement (MAKA) scheme was designed in 2019 to guarantee user privacy, but we found that the scheme does not withstand impersonation, stolen senor node and leaking verification table attacks, and it does not also ensure anonymity, untraceability and secure mutual authentication. This paper proposes a provably secure and lightweight MAKA scheme for medical IoT, called LAKS Non-verification table (NVT), that does not require a server verification table. We assess LAKS-NVT’s security against various potential attacks and demonstrate that it achieves secure MA between sensor node and server using Burrows-Abadi-Needham logic. We employ the well-known Real-Or-Random which is random oracle model to prove that LAKS-NVT provides a session key security. In addition, the formal security verification using the widely-accepted Automated Validation of Internet Security Protocols and Applications (AVISPA) software tool has been performed and the results show that LAKS-NVT is also secure. We compare LAKS-NVT’s performance against contemporary authentication schemes, and verify that it achieves better security and comparable efficiency. The practical perspective of LAKS-NVT is also carried out via the Network Simulator 2 (NS2) simulation study.

Preserving Balance Between Privacy and Data Integrity in Edge-Assisted Internet of Things

Internet of Things (IoT) devices and the edge jointly broaden the IoT’s sensing capability and the monitoring scope for various applications. Though accessing sensing data and making decisions through IoT smart devices turns out to be commonplace, it is challenging to guarantee user privacy and preserve the accuracy (integrity) of the collected data. The IoT smart devices frequently lose either IoT user’s privacy or data integrity. This also makes it crucial to put a threshold on the cost of computation and load of the IoT devices, as gradually more IoT services demand access to the resources that devices offer. In this article, we propose BalancePIC , a scheme that attempts to preserve a balance in the three aspects (user privacy, data integrity in edge-assisted IoT devices, and the computational cost). It achieves the balance through a balanced truth discovery approach and a proposed enhanced technique for data privacy, which are used in IoT devices and edge server interactions. It authenticates the IoT user participation with privacy in the truth discovery process through a biometric-ECC-based authentication algorithm. The nature of the BalancePIC scheme is to straightforwardly provide the likelihood for a simple amendment on the cryptography technique and weight assignment. This lessens the overall computational cost for the IoT user devices but also restricts the communications between the user devices and the edge server, which is important for data integrity. We present an enhanced technique to preserve privacy by guarding the user from potential threats and suspicious data collection parties. To achieve this, BalancePIC takes steps to blur the original sensory data of the device by processing results in groups called zones. Simulation result analysis provides evidence for the balance preservation in the three aspects.

An Enhanced Lightweight Dynamic PseudonymIdentity Based Authentication and Key AgreementScheme Using Wireless Sensor Networks forAgriculture Monitoring.

Agriculture plays an important role for many countries. It provides raw materials for food and provides large employment opportunities for people in the country, especially for countries with a dense population. To enhance agriculture productivity, modern technology such as wireless sensor networks (WSNs) can be utilized to help in monitoring important parameters in thw agricultural field such as temperature, light, soil moisture, etc. During the monitoring process, if security compromises happen, such as interception or modification of the parameters, it may lead to false decisions and bring damage to agriculture productivity. Therefore, it is very important to develop secure authentication and key agreement for the system. Recently, Ali et al. proposed an authentication and key agreement scheme using WSNs for agriculture monitoring. However, it fails to provide user untraceability, user anonymity, and session key security; it suffers from sensor node impersonation attack and perfect forward secrecy attack; and even worse has denial of service as a service. This study discusses these limitations and proposes a new secure and more efficient authentication and key agreement scheme for agriculture monitoring using WSNs. The proposed scheme utilizes dynamic pseudonym identity to guarantee user privacy and eliminates redundant computations to enhance efficiency.

Blockchain-Based Data Preservation System for Medical Data.

Medical care has become an indispensable part of people's lives, with a dramatic increase in the volume of medical data (e.g., diagnosis certificates and medical records). Medical data, however, is easily stolen, tampered with, or even completely deleted. If the above occurs, medical data cannot be recorded or retrieved in a reliable manner, resulting in delay treatment progress, even endanger the patient's life. In this paper, we propose a novel blockchain-based data preservation system (DPS) for medical data. To provide a reliable storage solution to ensure the primitiveness and verifiability of stored data while preserving privacy for users, we leverage the blockchain framework. With the proposed DPS, users can preserve important data in perpetuity, and the originality of the data can be verified if tampering is suspected. In addition, we use prudent data storage strategies and a variety of cryptographic algorithms to guarantee user privacy; e.g., an adversary is unable to read the plain text even if the data are stolen. We implement a prototype of the DPS based on the real world blockchain-based platform Ethereum. Performance evaluation results demonstrate the effectiveness and efficiency of the proposed system.

An Exception Handling Approach for Privacy-Preserving Service Recommendation Failure in a Cloud Environment.

Service recommendation has become an effective way to quickly extract insightful information from massive data. However, in the cloud environment, the quality of service (QoS) data used to make recommendation decisions are often monitored by distributed sensors and stored in different cloud platforms. In this situation, integrating these distributed data (monitored by remote sensors) across different platforms while guaranteeing user privacy is an important but challenging task, for the successful service recommendation in the cloud environment. Locality-Sensitive Hashing (LSH) is a promising way to achieve the abovementioned data integration and privacy-preservation goals, while current LSH-based recommendation studies seldom consider the possible recommendation failures and hence reduce the robustness of recommender systems significantly. In view of this challenge, we develop a new LSH variant, named converse LSH, and then suggest an exception handling approach for recommendation failures based on the converse LSH technique. Finally, we conduct several simulated experiments based on the well-known dataset, i.e., Movielens to prove the effectiveness and efficiency of our approach.

Efficient Anonymous Communication in SDN-Based Data Center Networks

With the rapid growth of application migration, the anonymity in data center networks becomes important in breaking attack chains and guaranteeing user privacy. However, existing anonymity systems are designed for the Internet environment, which suffer from high computational and network resource consumption and deliver low performance, thus failing to be directly deployed in data centers. In order to address this problem, this paper proposes an efficient and easily deployed anonymity scheme for software defined networking-based data centers, called mimic channel (MIC). The main idea behind MIC is to conceal the communication participants by modifying the source/destination addresses, such as media access control (MAC) and Internet protocol (IP) address at switch nodes, so as to achieve anonymity. Compared with the traditional overlay-based approaches, our in-network scheme has shorter transmission paths and less intermediate operations, thus achieving higher performance with less overhead. We also propose a collision avoidance mechanism to ensure the correctness of routing, and three mechanisms to enhance the traffic-analysis resistance. To enhance the practicality, we further propose solutions to enable MIC co-existing with some MIC-incompatible systems, such as packet analysis systems, intrusion detection systems, and firewall systems. Our security analysis demonstrates that MIC ensures unlinkability and improves traffic-analysis resistance. Our experiments show that MIC has extremely low overhead compared with the base-line transmission control protocol (TCP) (or secure sockets layer (SSL)), e.g., less than 1% overhead in terms of throughput. Experiments on MIC-based distributed file system show the applicability and efficiency of MIC.

Trust-Based Cooperative Social System Applied to a Carpooling Platform for Smartphones.

One of the worst traffic problems today is the existence of huge traffic jams in almost any big city, produced by the large number of commuters using private cars. This problem has led to an increase in research on the optimization of vehicle occupancy in urban areas as this would help to solve the problem that most cars are occupied by single passengers. The solution of sharing the available seats in cars, known as carpooling, is already available in major cities around the world. However, carpooling is still not considered a safe and reliable solution for many users. With the widespread use of mobile technology and social networks, it is possible to create a trust-based platform to promote carpooling through a convenient, fast and secure system. The main objective of this work is the design and implementation of a carpool system that improves some important aspects of previous systems, focusing on trust between users, and on the security of the system. The proposed system guarantees user privacy and measures trust levels through a new reputation algorithm. In addition to this, the proposal has been developed as a mobile application for devices using the Android Open Source Project.