Privacy-preserving Reputation Research Articles

A Systematic Review of Blockchain-based Privacy-Preserving Reputation Systems for IoT Applications

A Systematic Review of Blockchain-based Privacy-Preserving Reputation Systems for IoT Applications

PPRU: A Privacy-Preserving Reputation Updating Scheme for Cloud-Assisted Vehicular Networks

PPRU: A Privacy-Preserving Reputation Updating Scheme for Cloud-Assisted Vehicular Networks

A Privacy-Preserving and Reputation-Based Truth Discovery Framework in Mobile Crowdsensing

In mobile crowdsensing (MCS), truth discovery (TD) plays an important role in sensing task completion. Most of the existing studies focus on the privacy preservation of mobile users, and the reliability of mobile users is evaluated by their weights which are calculated based on the submitted sensing data. However, if mobile users are unreliable, the submitted sensing data and their weights are also unreliable, which may influence the accuracy of the ground truths of sensing tasks. Therefore, this paper proposes a privacy-preserving and reputation-based truth discovery framework named PRTD which can generate the ground truths of sensing tasks with high accuracy while preserving privacy. Specifically, we first preserve sensing data privacy, weight privacy, and reputation value privacy by utilizing the Paillier algorithm and Pedersen commitment. Then, to verify whether the reputation values of mobile users are tampered with and select mobile users that satisfy the corresponding reputation requirements, we design a privacy-preserving reputation verification algorithm based on reputation commitment and zero-knowledge proof and propose a concept of reliability level to select mobile users. Finally, a general TD algorithm with reliability level is presented to improve the accuracy of the ground truths of sensing tasks. Moreover, theoretical analysis and performance evaluation are conducted, and the evaluation results demonstrate that the PRTD framework outperforms the existing TD frameworks in several evaluation metrics in the synthetic dataset and real-world dataset.

VRepChain: A Decentralized and Privacy-Preserving Reputation System for Social Internet of Vehicles Based on Blockchain

In the context of the social Internet of vehicles (SIoV), constructing reliable social relationships between dynamic and distributed entities is a challenging research problem. Rating-based reputation systems have been widely applied to assist human users in evaluating the honesty of target entities. However, the ratings in SIoV expose user privacy, including behavior, location, etc., which are required to be protected properly. Meanwhile, the blockchain technology with its distributed paradigm is potentially employed to protect information privacy. In this study, we propose the design of a blockchain-enabled reputation system named “VRepChain” for SIoV by especially considering the rating privacy issue. In our design, the ratings' privacy is strongly preserved in the processes of transmission and storage. The reputation of a vehicle is constructed based on the ratings with the agreement of the rating providers, ensuring the ratings are never abused by any other unauthorized entities during the usage process. Through experiments, the proposed system is demonstrated to improve the effectiveness of vehicles in terms of arriving at their destinations in a faster speed. Furthermore, the effectiveness of the constructed reputation model with untruthful ratings is extensively examined, showing its robustness and practicality in realistic applications.

Resisting bad mouth attack in vehicular platoon using node-centric weight-based trust management algorithm (NC-WTM)

Individuals use Vehicular Ad Hoc Networks (VANETs) to secure, reliable communication networks as a viable route of transmission. Platooning uses Inter-Vehicular Communication to supervise a group of vehicles (IVC). The information communicated, especially acceleration, location, and speed, can then be used to respond by longitudinal control legislation. Platoons require help between those vehicles to improve fuel efficiency, security, and various crises associated with driver errors. Despite these advances, the communication network must govern vehicles unprotected from adequate threat vectors, which malicious attacks might exploit. Considering that connected cars between trusts could increase security, whereas all active automobiles can develop and spread legitimate, accurate, and recognized information within the network. So, considering badmouth, this work proposes a unique node centric weight-based trust management algorithm (NC-WTM). By quickly recognising and deleting fraudulent vehicles and their generated messages, the NC-WTM (node-centric weighted trust management) technique improves aggregate platoon security. The NC-WTM beats the robust and privacy-preserving reputation management scheme (RPRep) and the attack resistant trust management scheme (ART) trust designs regarding accuracy, recall, and F-score. Metrics, such as precision results of 78%, recall of 69.3%, F-score of 60.4%, and accuracy of 89%, are achieved and optimised in this trust model. Abbreviations: VANETs: vehicular ad hoc networks; IVC: inter-vehicular communication; NC-WTM: node-centric weight-based trust management algorithm; WTM: weight-based trust management algorithm; RPRep: robust and privacy-preserving reputation management scheme; ART: attack-resistant trust management scheme; MANET: mobile ad hoc network; DSRC: dedicated short-range communication; WAVE: wireless access in vehicular environment; IVC: inter-vehicular communication; I2V: infrastructure-to-vehicle; V2I: vehicle-to-infrastructure; V2V: vehicle-to-vehicle; TA: trust authority; RSU: road side unit; OBU: on-board unit; GPS: global positioning system; WSN: wireless sensor network; VASNETs: vehicular sensor networks; CCW: cooperative collision warning; BMA: bad mouth attack; TDMA: time division multiple access; GDVAN: greedy detection for VANETs; SMTS: spider monkey time synchronization; SVM: support vector machine; DST: Dempster-Shafer theory of evidence; TA: trust authority; PCA: puzzle-based co-authentication; VLC: visible light communication; NE: Nash equilibrium; RTB: request-to-broadcast; CTB: clear-to-broadcast; RREQ: route request message; RREP: route reply; DDR: data disseminate ratio; Dir: direct trust; IDir: indirect trust; TCE: trust computation error; PDR: packet delivery ratio

Privacy-Preserving Reputation Systems Based on Blockchain and Other Cryptographic Building Blocks: A Survey

The purpose of a reputation system is to hold the users of a distributed application accountable for their behavior. The reputation of a user is computed as an aggregate of the feedback provided by fellow users in the system. Truthful feedback is clearly a prerequisite for computing a reputation score that accurately represents the behavior of a user. However, it has been observed that users can hesitate in providing truthful feedback because, for example, of fear of retaliation. Privacy-preserving reputation systems enable users to provide feedback in a private and thus uninhibited manner. In this survey, we propose analysis frameworks for privacy-preserving reputation systems. We use these analysis frameworks to review and compare the existing approaches. Emphasis is placed on blockchain-based systems as they are a recent significant development in the area. Blockchain-based privacy-preserving reputation systems have properties, such as trustlessness, transparency, and immutability, which prior systems do not have. Our analysis provides several insights and directions for future research. These include leveraging blockchain to its full potential in order to develop truly trustless systems, to achieve some important security properties, and to include defenses against common attacks that have so far not been addressed by most current systems.

A Lightweight Privacy Preservation Scheme with Efficient Reputation Management for Mobile Crowdsensing in Vehicular Networks

Mobile crowdsensing (MCS) refers to a group of mobile users utilizing their sensing devices to accomplish the same sensing task. However, in vehicular networks, how to evaluate the reliability of sensing vehicles and achieve lightweight privacy preservation are urgent issues. Therefore, this paper proposes a lightweight privacy preservation scheme with efficient reputation management (PPRM) for MCS in vehicular networks. Specifically, we design a lightweight privacy-preserving sensing task matching algorithm which can preserve the location privacy, identity privacy, sensing data privacy, and reputation value privacy while reducing communication and computation overheads of sensing vehicles. In particular, to prevent reputation values from being forged and select reliable sensing vehicles, we present a privacy-preserving reputation value equality verification algorithm to verify reputation values and a privacy-preserving reputation value range proof algorithm to choose sensing vehicles. Afterwards, a three-factor reputation value update algorithm is constructed to efficiently and accurately update the reputation values for sensing vehicles. Simulations are conducted to demonstrate the performance of the PPRM scheme, and the results show that the PPRM scheme significantly outperforms the existing schemes in security and robustness aspects.

Anonymous and Verifiable Reputation System for E-Commerce Platforms Based on Blockchain

E-commerce platforms incorporate reputation systems that allow customers to rate suppliers following financial transactions. Existing reputation systems cannot defend the centralized server against arbitrarily tampering with the supplier’s reputation. Furthermore, they do not offer reputation access across platforms. Rates are faced with privacy leakages because rating activities are correlated with privacy (e.g., identity and rating). Meanwhile, raters could be malicious and initiate multiple rating attacks and abnormal rating attacks. Determining how to address these issues have both research and practical value. In this paper, we propose a blockchain-based privacy-preserving reputation system for e-commerce platforms named RepChain; our system allows cross-platform reputation access and anonymous and private ratings. Using RepChain, all e-commerce platforms collaborate and share users’ reputations by co-constructing a consortium blockchain and modeling the rating process as a finite state machine. In particular, we facilitate one-show anonymous credentials constructed from two-move blind signatures to protect customers’ identities and resist multiple rating attacks, leverage zero-knowledge range proof to verify the correctness of ratings and defend against abnormal rating attacks, design a secure sum computation protocol among nodes to update reputations, and verify ratings via batch processing and consensus hashes. Finally, we demonstrate the security and privacy of RepChain via a formal analysis and evaluate its performance based on Ethereum test network.

Privacy Preserving Reputation Calculation in P2P Systems with Homomorphic Encryption

In this paper, we consider the problem of calculating the node reputation in a Peer-toPeer (P2P) system from fragments of partial knowledge concerned with the trustfulness of nodes which are subjectively given by each node (i.e., evaluator) participating in the system. We are particularly interested in the distributed processing of the calculation of reputation scores while preserving the privacy of evaluators. The basic idea of the proposed method is to extend the EigenTrust reputation management system with the notion of homomorphic cryptosystem. More specifically, it calculates the main eigenvector of a linear system which models the trustfulness of the users (nodes) in the P2P system in a distributed manner, in such a way that: 1) it blocks accesses to the trust value by the nodes to have the secret key used for the decryption, 2) it improves the efficiency of calculation by offloading a part of the task to the participating nodes, and 3) it uses different public keys during the calculation to improve the robustness against the leave of nodes. The performance of the proposed method is evaluated through numerical calculations.

SoK: Privacy-Preserving Reputation Systems

Abstract Trust and user-generated feedback have become increasingly vital to the normal functioning of the modern internet. However, deployed systems that currently incorporate such feedback do not guarantee users much in the way of privacy, despite a wide swath of research on how to do so spanning over 15 years. Meanwhile, research on systems that maintain user privacy while helping them to track and update each others’ reputations has failed to standardize terminology, or converge on what privacy guarantees should be important. Too often, this leads to misunderstandings of the tradeoffs underpinning design decisions. Further, key insights made in some approaches to designing such systems have not circulated to other approaches, leaving open significant opportunity for new research directions. This SoK investigates 42 systems describing privacy-preserving reputation systems from 2003–2019 in order to organize previous work and suggest directions for future work. Our three key contributions are the systematization of this body of research, the detailing of the tradeoffs implied by overarching design choices, and the identification of underresearched areas that provide promising opportunities for future work.

BPRF: Blockchain-based privacy-preserving reputation framework for participatory sensing systems.

Participatory sensing is gaining popularity as a method for collecting and sharing information from distributed local environments using sensor-rich mobile devices. There are a number of participatory sensing applications currently in wide use, such as location-based service applications (e.g., Waze navigation). Usually, these participatory applications collect tremendous amounts of sensing data containing personal information, including user identity and current location. Due to the high sensitivity of this information, participatory sensing applications need a privacy-preserving mechanism, such as anonymity, to secure and protect personal user data. However, using anonymous identifiers for sensing sources proves difficult when evaluating sensing data trustworthiness. From this perspective, a successful participatory sensing application must be designed to consider two challenges: (1) user privacy and (2) data trustworthiness. To date, a number of privacy-preserving reputation techniques have been proposed to satisfy both of these issues, but the protocols contain several critical drawbacks or are impractical in terms of implementation. In particular, there is no work that can transparently manage user reputation values while also tracing anonymous identities. In this work, we present a blockchain-based privacy-preserving reputation framework called BPRF to transparently manage user reputation values and provide a transparent tracing process for anonymous identities. The performance evaluation and security analysis show that our solution is both practical and able to satisfy the two requirements for user privacy and data trustworthiness.

Dynamic and Privacy-Preserving Reputation Management for Blockchain-Based Mobile Crowdsensing

Mobile crowdsensing (MCS) is an emerging data collection paradigm that exploits the potential of individual mobile devices to acquire mass data in a cost-effective manner. One of the important challenges in MCS application is to resist malicious users who provide false data to disturb the system. In the existing work, the reputation management scheme is an effective way to overcome the challenge. However, most reputation management schemes rely on a semi-honest server and process data in the plaintext domain without considering server security and user privacy. In this paper, we integrate the blockchain and edge computing in the MCS scenario to construct a credible and efficient blockchain-based MCS system, called BC-MCS. To resist malicious users, we present a privacy-preserving reputation management scheme based on the proposed system. Furthermore, we design a delegation protocol to solve the inherent problem of user dynamics in the MCS. The prototype system implemented on the Hyperledger Sawtooth and Android client demonstrates that our scheme can achieve higher utility and security levels in handling malicious users compared with the previous centralized reputation management schemes.

M2M-REP: Reputation system for machines in the internet of things

In the age of IoT (Internet of Things), Machine-to-Machine (M2M) communication has gained significant popularity over the last few years. M2M communication systems may have a large number of autonomous connected devices that provide services without human involvement. Interacting with compromised, infected and malicious machines can bring damaging consequences in the form of network outage, machine failure, data integrity, and financial loss. Hence, users first need to evaluate the trustworthiness of machines prior to interacting with them. This can be realized by using a reputation system, which evaluates the trustworthiness of machines by utilizing the feedback collected from the users of the machines. The design of a reliable reputation system for the distributed M2M communication network should preserve user privacy and have low computation and communication overheads. To address these challenges, we propose an M2M-REP System (Machine to Machine REPutation), a privacy-preserving reputation system for evaluating the trustworthiness of autonomous machines in the M2M network. The system computes global reputation scores of machines while maintaining privacy of the individual participant score by using secure multi-party computation techniques. The M2M-REP system ensures correctness, security and privacy properties under the malicious adversarial model, and allows public verifiability without relying on a centralized trusted system. We implement a prototype of our system and evaluate the system performance in terms of the computation and bandwidth overhead.

Dynamic, Privacy-Preserving Decentralized Reputation Systems

Reputation systems provide an important basis for judging whether to interact with others in a networked system. Designing such systems is especially interesting in decentralized environments such as mobile ad-hoc networks, as there is no trusted authority to manage reputation feedback information. Such systems also come with significant privacy concerns, further complicating the issue. Researchers have proposed privacy-preserving decentralized reputation systems (PDRS) which ensure individual reputation information is not leaked. Instead, aggregate information is exposed. Unfortunately, in existing PDRS, when a party leaves the network, all of the reputation information they possess about other parties in the network leaves too. This is a significant problem when applying such systems to the kind of dynamic networks we see in mobile computing. In this article, we introduce dynamic, privacy-preserving reputation systems (Dyn-PDRS) to solve the problem. We enumerate the features that a reputation system must support in order to be considered a Dyn-PDRS. Furthermore, we present protocols to enable these features and describe how our protocols are composed to form a Dyn-PDRS. We present simulations of our ideas to understand how a Dyn-PDRS impacts information availability in the network, and report on an implementation of our protocols, including timing experiments.

A privacy-preserving reputation system with user rewards

Reputation systems are useful to assess the trustworthiness of potential transaction partners, but also a potential threat to privacy since rating profiles reveal users’ preferences. Anonymous reputation systems resolve this issue, but make it difficult to assess the trustworthiness of a rating. We introduce a privacy-preserving reputation system that enables anonymous ratings while making sure that only authorized users can issue ratings. In addition, ratings can be endorsed by other users. A user who has received a pre-defined number of endorsements can prove this fact, and be rewarded e.g. by receiving a “Premium member” status. The system is based on advanced cryptographic primitives such as Chaum-Pedersen blind signatures, verifiable secret sharing and oblivious transfer.

Recommender Privacy Preserving Reputation Based Medical Services Scheme Using a Variant of ElGamal

Recommender Privacy Preserving Reputation Based Medical Services Scheme Using a Variant of ElGamal

Privacy Preserving Reputation Based Medical Services Scheme Using a Variant of ElGamal

Reputation Based schemes are gaining popularity in all spheres of the service industry. In this paper, we have proposed a privacy preserving secure reputation based medical services system. This system enables Medical Users to get a reputation score of a Medical Service Provider before engaging in any services. This kind of approach ensures that the Medical User has some apriori information of the quality of service to expect from a Medical Service Provider. Our proposed scheme uses a variant of the ElGamal cryptosystem. It is privacy preserving in the sense that both the Medical User making the query and the Medical Service Provider remain anonymous throughout the process. Compared to other reputation based schemes our scheme offers more security services than most existing schemes.

Towards privacy-preserving reputation management for hybrid broadcast broadband applications

Hybrid Broadcast Broadband TV (HbbTV) is an industry standard aimed to provide a platform combining TV services with Internet services, using connected TVs and set-top boxes. It enables the possibility for vendors to offer applications directly to the users, introducing new entertainment services such as streaming of video on demand, games, social networking, etc. As a consequence, tons of applications are available for users to directly download and consume through the so-called application stores, despite the potential trust and security issues arising due to the decentralized nature of these environments.Reputation management systems are usually deployed to handle trust in such dynamic scenarios, whereas they could also be used to evaluate and rate the applications from the users' point of view, and even provide customized rankings. Nevertheless, they require the application stores to know information related to the installed applications and the provided recommendations of the users, hence compromising their privacy.In this paper we present a privacy-preserving reputation management framework to be integrated within the HbbTV context. We make use of identity management and extend homomorphic encryption techniques to avoid the application stores and other relying parties determining the recommendations provided by the users, yet being able to compute customized reputation values based on the similarity between users' recommendations.

On Joint Privacy and Reputation Assurance for Vehicular Ad Hoc Networks

In vehicular ad hoc networks (VANETs), privacy protection makes it challenging to maintain a long-term reputation of any node, while reputation management requires reputation manifestation at risk of easier vehicle tracking. Here, JPRA is proposed to reconcile these challenging conflicts and support the synergistic coexistence of both indispensible schemes in VANETs. JPRA adopts a localized reputation management model where the behavior evaluation, reputation aggregation, and reputation manifestation of each node are collectively performed by itself and its neighbors. In this model, novel algorithms are designed to support secure and efficient reputation management in face of node mobility and privacy protection. Furthermore, a conditional reputation discretization algorithm ensures privacy-preserving reputation manifestation for the honest nodes. Theoretical analysis and simulations show that JPRA can efficiently and synergistically support both schemes in VANETs.

Efficient Privacy-Preserving Reputation Evaluation in Decentralized Environments

Efficient Privacy-Preserving Reputation Evaluation in Decentralized Environments