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2,533 results · page 84 of 106
Tommaso Crepax, Siddharth Prakash Rao
No abstract is available for this record.
Xiaoyu Zhu, Yi Li, Fang Li, Ping Chen
In online crowdsourcing services, credible accountability mechanisms are crucial for guaranteeing a good interactive environment. However, the crowdsourcing systems are established in virtual environments, the identities of the participants are various and complicated, the systems could scarcely identify malicious nodes automatically. So it is very hard to preserve the complete evidence of malicious behaviors and investigate relevant legal responsibilities. Blockchain is regarded as a very promising solution to these problems because it possesses characteristics of decentration, non-modifiability and traceability. However, a main challenge is to design an applicable blockchain consensus algorithm which can reach an agreement on credibility of participants automatically, prevent transaction data from tampering, and trace to the source of malicious behaviors. In this paper, an improved Proof-of-Trust (PoT) consensus scheme is proposed with the underlying technology of blockchain, which is properly to the crowdsourcing service scenarios. Firstly, this PoT consensus selects nodes with high credibility using subjective logic reputation algorithm. Only selected nodes have the chance to generate blocks, participate in verification, and claim crowdsourcing tasks. Secondly, the choice scheme of generate-block nodes is further optimized through the unpredictability of timestamp and digital signature. Moreover, an incentive mechanism based on game theory is designed in this consensus. With this mechanism, candidate nodes prefer to give honest verification results rather than engage in collusion with malicious nodes. The analysis and simulation results demonstrate the effectiveness, feasibility and scalability of the proposed approach.
Amjad Saeed Khan, Gaojie Chen, Yogachandran Rahulamathavan, Gan Zheng · 6 authors
The UAV is emerging as one of the greatest technology developments for rapid network coverage provisioning at affordable cost. The aim of this paper is to outsource network coverage of a specific area according to a desired quality of service requirement and to enable various entities in the network to have intelligence to make autonomous decisions using blockchain and auction mechanisms. In this regard, by considering a multiple-UAV network where each UAV is associated to its own controlling operator, this paper addresses two major challenges: the selection of the UAV for the desired quality of network coverage and the development of a distributed and autonomous real-time monitoring framework for the enforcement of service level agreement (SLA). For a suitable UAV selection, we employ a reputation-based auction mechanism to model the interaction between the business agent who is interested in outsourcing the network coverage and the UAV operators serving in closeby areas. In addition, theoretical analysis is performed to show that the proposed auction mechanism attains a dominant strategy equilibrium. For the SLA enforcement and trust model, we propose a permissioned blockchain architecture considering Support Vector Machine (SVM) for real-time autonomous and distributed monitoring of UAV service. In particular, smart contract features of the blockchain are invoked for enforcing the SLA terms of payment and penalty, and for quantifying the UAV service reputation. Simulation results confirm the accuracy of theoretical analysis and efficacy of the proposed model.
Max Hoffmann, Michael Klooß, Markus Raiber, Andy Rupp
Abstract Black-box accumulation (BBA) is a building block which enables a privacy-preserving implementation of point collection and redemption, a functionality required in a variety of user-centric applications including loyalty programs, incentive systems, and mobile payments. By definition, BBA+ schemes (Hartung et al. CCS ‘17) offer strong privacy and security guarantees, such as unlinkability of transactions and correctness of the balance flows of all (even malicious) users. Unfortunately, the instantiation of BBA+ presented at CCS ‘17 is, on modern smartphones, just fast enough for comfortable use. It is too slow for wearables, let alone smart-cards. Moreover, it lacks a crucial property: For the sake of efficiency, the user’s balance is presented in the clear when points are deducted. This may allow to track owners by just observing revealed balances, even though privacy is otherwise guaranteed. The authors intentionally forgo the use of costly range proofs, which would remedy this problem. We present an instantiation of BBA+ with some extensions following a different technical approach which significantly improves efficiency. To this end, we get rid of pairing groups, rely on different zero-knowledge and fast range proofs, along with a slightly modified version of Baldimtsi-Lysyanskaya blind signatures (CCS ‘13). Our prototype implementation with range proofs (for 16 bit balances) outperforms BBA+ without range proofs by a factor of 2.5. Moreover, we give estimates showing that smart-card implementations are within reach.
Bin Liu, Lijun Xiao, Jing Long, Mingdong Tang · 5 authors
Previous contract protocols in blockchains ensure their fairness and traceability by utilizing centralized credible nodes. If credible nodes are dishonest or conspire with the signatory, then other nodes are compromised. Meanwhile, the leakage of sensitive information of participant nodes poses a serious threat to the privacy security of data access in blockchains. To address this issue, this study proposes a secure control method of digital certificate-based data access in blockchains. The proposed method combines blockchain and digital certificate technologies and designs a secure authentication protocol for privacy data in blockchains without verifying the encrypted identity signature of the third-party participant. The high-efficiency network forwarding protocol proposed in this work can support the fair contract signing of multiple signers via blockchain. This protocol can protect the privacy of contracts and identities of participants. Experimental results show that the proposed scheme is superior in terms of communication overhead, storage overhead, and detection rate.
Aditya Damodaran, Alfredo Rial
No abstract is available for this record.
Qianlong Wang, Tianxi Ji, Yifan Guo, Lixing Yu · 6 authors
Intelligent Connected Vehicles (ICVs) can provide smart, safe, and efficient transportation services and have attracted intensive attention recently. Obtaining timely and accurate traffic information is one of the most important problems in transportation systems, which would allow people to select fast routes and avoid congestions, thus saving their travel time on the road. Currently, the most popular ways to obtain traffic information is to inquire navigation agents, e.g., Apple map, and Google map. However, these navigation agents are essentially centralized systems, which are vulnerable to service congestions, a single point of failure, and attacks. Furthermore, users' privacy gets compromised as the agents can know their home and work addresses and hence their identities, track them in real-time, etc. In this paper, we propose TrafficChain, a secure and privacy-preserving decentralized traffic information collection system on the blockchain, by taking advantage of fog/edge computing infrastructure. In particular, we employ a two-layer blockchain architecture in TrafficChain to improve system efficiency, design a privacy-preserving scheme to protect users' identities and travel traces, and devise LSTM based deep learning mechanisms that can defend against Byzantine attacks and Sybil attacks in our system. Furthermore, an incentive mechanism is designed to motivate users to participate in the system. Simulation results show that TrafficChain works very efficiently and is resilient to both Byzantine attacks and Sybil attacks.
Sébastien Canard, Adel Hamdi, Fabien Laguillaumie
No abstract is available for this record.
Aditya Damodaran, Alfredo Rial
No abstract is available for this record.
Jayamine Alupotha, Xavier Boyen, Ernest Foo
No abstract is available for this record.
Zhaohua Chen, Guang Yang
Custody is a core financial service in which the custodian holds in safekeeping assets on behalf of the client. Although traditional custody service is typically endorsed by centralized authorities, decentralized custody scheme has become technically feasible since the emergence of digital assets, and furthermore it is badly needed by new applications such as blockchain and DeFi (Decentralized Finance). In this work, we propose a framework of decentralized asset custody scheme that is able to support a large number of custodians and safely hold customer assets of multiple times value of the total security deposit. The proposed custody scheme distributes custodians and assets into many custodian groups via combinatorial designs and random sampling, where each group fully controls the assigned assets. Since every custodian group is small, the overhead cost is significantly reduced. The liveness is also improved because even a single alive group would be able to process transactions. The security of this custody scheme is guaranteed in the game-theoretic sense, such that any adversary corrupting a bounded fraction of custodians cannot move assets more than his own security deposit. We further analyze the security and performance of our constructions, and give explicit examples with concrete numbers and figures for a better understanding of our results.
Nadia Pocher
This research paper focuses on the interconnections between traditional and cutting-edge technological features of virtual currencies and the EU legal framework to prevent the misuse of the financial system for money laundering and terrorist financing purposes. It highlights a set of Anti-Money Laundering and Counter-Terrorist Financing (AML/CFT) challenges brought about in the Internet of Money (IoM) landscape by the double-edged nature of Distributed Ledger Technologies (DLTs) as both transparency and privacy ori- ented. Special attention is paid to inferences from concepts such as pseudonymity and traceability; this contribution explores these notions by relating them to privacy enhanc- ing mechanisms and blockchain intelligence strategies, while heeding both core elements of the present AML/CFT obliged entities’ framework and possible new conceptualizations. Finally, it identifies key controversies and open questions as to the actual feasibility of ef- fectively applying the “active cooperation” AML/CFT approach to the crypto ecosystems.
Nasim Al Goni, Sherif Saad Ahmed, Ahmed Ibrahim
No abstract is available for this record.
R. Carlsson
There is a potential in the field of medicine and finance of doing collaborative machine learning. These areas gather data which can be used for developing machine learning models that could predict all from sickness in patients to acts of economical crime like fraud. The problem that exists is that the data collected is mostly of confidential nature and should be handled with precaution. This makes the standard way of doing machine learning - gather data at one centralized server - unwanted to achieve. The safety of the data have to be taken into account. In this project we will explore the Federated learning approach of ”bringing the code to the data, instead of data to the code”. It is a decentralized way of doing machine learning where models are trained on connected devices and data is never shared. Keeping the data privacypreserved.
Norbert Jastroch
No abstract is available for this record.
Omar El Rifai, Maëlle Biotteau, X. De Boissezon, Imen Megdiche · 6 authors
No abstract is available for this record.
Fahad Ahmed Al-Zahrani
In modern times, many individuals, businesses and the Internet of Things (IoT) integrated industries collect huge amounts of meaningful data daily, which may be beneficial for other individuals and businesses as well. By utilizing this data, future trends to make the right decisions on the bases of facts and figures are analyzed efficiently. In addition to that, many new ways are paved for researchers to utilize this data in their upcoming research. However, due to some major issues like security, privacy and access control of data, data owners avoid sharing data among themselves. Another main problem is the selfish behavior of data owners. Businesses also act selfishly and invest huge amounts of money to collect and maintain the data for their benefits. Therefore, data owners are hesitant to share their data with others without the availability of a fair profit and secure data-sharing platform. Moreover, consumers are not much motivated to buy data from Data Providers (DPs) due to its bad quality and inconsistency. The data provided by data owners is mostly incomplete, outdated, heterogeneous and costly. In this paper, a subscription-based data-sharing model is proposed by leveraging the blockchain technology and Data as a Service (DaaS) concept. In this model, users subscribe to a DP for a specific period to get access to the data and pay according to the subscription plan. The DP keeps receiving revenue recurrently for a long-time, which has a huge profit margin in comparison with selling data at once. Furthermore, two major pricing models, Flat Rate Pricing (FRP) and Usage-Based Pricing (UBP), are discussed to set standards for data owners to monetize their data, and a new hybrid pricing model is also proposed. Blockchain technology is utilized in the proposed model to make it secure, transparent and immutable. To investigate the performance of the proposed model, a private blockchain network is deployed using a web interface provided by MultiChain blockchain. The simulation results demonstrate that the proposed model is feasible and efficient. The theoretical discussion proves that the proposed model is beneficial for both data owners and data consumers and has a good scope in the future for data management and trading processes.
Michele Ciampi, Roberto Parisella, Daniele Venturi
We study adaptive security of delayed-input Sigma protocols and non-interactive zero-knowledge (NIZK) proof systems in the common reference string (CRS) model. Our contributions are threefold: We exhibit a generic compiler taking any delayed-input Sigma protocol and returning a delayed-input Sigma protocol satisfying adaptive-input special honest-verifier zero knowledge (SHVZK). In case the initial Sigma protocol also satisfies adaptive-input special soundness, our compiler preserves this property. We revisit the recent paradigm by Canetti et al. (STOC 2019) for obtaining NIZK proof systems in the CRS model via the Fiat-Shamir transform applied to so-called trapdoor Sigma protocols, in the context of adaptive security. In particular, assuming correlation-intractable hash functions for all sparse relations, we prove that Fiat-Shamir NIZKs satisfy either: (i) Adaptive soundness (and non-adaptive zero knowledge), so long as the challenge is obtained by hashing both the prover’s first round and the instance being proven; (ii) Adaptive zero knowledge (and non-adaptive soundness), so long as the challenge is obtained by hashing only the prover’s first round, and further assuming that the initial trapdoor Sigma protocol satisfies adaptive-input SHVZK. We exhibit a generic compiler taking any Sigma protocol and returning a trapdoor Sigma protocol. Unfortunately, this transform does not preserve the delayed-input property of the initial Sigma protocol (if any). To complement this result, we also give yet another compiler taking any delayed-input trapdoor Sigma protocol and returning a delayed-input trapdoor Sigma protocol with adaptive-input SHVZK.
Moritz Tobias Bruckner, Adeline Frenzel, Daniel Veit
Amazon Mechanical Turk (MTurk) allows organizations and individuals to benefit from a collective source of intelligence, skills and insights from a global population, but MTurk withdraws from responsibilities and the obligation of overlooking payment transactions. Consequently, there is no contractual agreement between the crowd worker and the requester on when payments should be authorized. Requesters even obtain ownership of the work without having to pay for it. This situation of lock-up poses a serious issue for crowd workers. As a solution to this problem, the present study introduces smart contracts to transactions on crowd work platforms, arguing that smart contracts can mitigate risks from lock-up situations and support trust, through non-alterable terms and conditions imprinted into the code of a smart contract. This research conceptualizes an online experimental study to validate the trust building and risk mitigating effects of smart contracts in crowd work transactions on Amazon Mechanical Turk.
Xu Hong, Qian He, Xuecong Li, Bingcheng Jiang · 5 authors
Aiming at the problem of privacy leakage during data sharing in the Internet of Things, a blockchain based secure data sharing platform with fine-grained access control(BSDS-FA) is proposed. First, this paper proposes a new hierarchical attribute-based encryption algorithm, which uses hierarchical attribute structure and multi-level authorization center. The algorithm implements flexible and fine-grained access control by distributing different user attributes to different authorization centers. Then, it combined with the Fabric blockchain technology to solve the problem of huge decryption cost for users in the Internet of things. Smart contract in blockchain executes high-complexity partial decryption algorithm to reduce the users' decryption overhead. Blockchain can also realize the traceability of historical operations to meet the security requirements of data restriction open and transparent supervision. Finally, the hierarchical attribute-based encryption algorithm is proved to be CPA-safe. The theoretical analysis and experimental results show that BDSS-FA provides more secure and reliable data sharing services for users in the Internet of Things.
Besfort Shala, Ulrich Trick, Armin Lehmann, Bogdan Ghita · 5 authors
Building trust relationships between different decentralized entities in the IoT ecosystem is essential. Hereof, the combination of blockchain technology and trust evaluation techniques is recently considered as an efficient measure. However, both technologies within the IoT are still facing some limitations which are addressed in this research. First, this publication reviews various blockchain-based trust approaches and depicts their strengths and limitations regarding their usage in decentralized IoT communities. Then, an optimized trust model with a multi-layer adaptive and trust-based weighting system is proposed. Additionally, different trust metric parameters and their mathematical models used for trust evaluation are presented. Moreover, this publication presents a novel approach for incentivization processes in the IoT marketplace using control loops and smart contracts. Thereby, participants are motivated to continuously improve their behavior. Finally, the proposed trust model is proved to be reliable. The experimental results conducted from different scenarios show that the presented approach provides more resiliency against various attacks than existing ones.
Sergey Zapechnikov
The article deals with the problems of cryptographic protection of data processing algorithms and techniques. They are novel techniques allowing to process private information without disclosing it to persons engaged in processing. One of the main applications of such security tools is the creation of personalized information services, which opens up new opportunities for business and reduces the risks of unauthorized access to personal data. We review important building blocks for cryptographic protection of data processing, such as zero-knowledge proofs, secure multi-party computations, and homomorphic encryption. Often, personalized information services are based on data mining and machine learning, so privacy-preserved machine learning is a very important building block for them. We analyze the concept of differential privacy which serves as the basis for privacy-preserving machine learning and some other cryptographic schemes. At the end of the paper, we forecast the perspectives of encrypted data processing.
Abdelatif Hafid, Abdelhakim Hafid, Mustapha Samih
Cryptocurrencies (e.g., Bitcoin and Ethereum), which promise to become the future of money transactions, are mainly implemented with blockchain technology. However, blockchain suffers from scalability issues. Sharding is the leading solution for blockchain scalability. Sharding splits the blockchain network into sub-chains called shards/committees. Each shard processes a sub-set of transactions, rather than the entire network processing all transactions. This raises security issues for sharding-based blockchain protocols. In this paper, we propose a novel methodology to analyze the security of these protocols (e.g., OmniLedger and RapidChain). In particular, this methodology estimates the failure probability of one sharding round taking into consideration the failure probabilities of all shards. To illustrate the effectiveness of the estimated failure probability, we conduct a numerical analysis of our methodology based on a huge number of trials. Finally, we compute confidence intervals to accurately estimate the failure probability and compare our methodology with existing approaches.
Liang Tan, Na Shi, Caixia Yang, Keping Yu
Cyber-Physical-Social System (CPSS) big data is specified as the global historical data which is usually stored in cloud, the local real-time data which is usually stored in the fog-edge server (FeS) of the mobile terminal devices or sensors, and the social data which is usually stored in the social data server (SdS), moreover adopts a centralized access control mechanism to offer users' access strategy which can easily cause CPSS big data to be tampered with and to be leaked. Therefore, a blockchain-based access control scheme called BacCPSS for CPSS big data is proposed. In BacCPSS, account address of the node in blockchain is used as the identity to access CPSS big data, the access control permission for CPSS big data is redefined and stored in blockchain, and processes of authorization, authorization revocation, access control and audit in BacCPSS are designed, and then a lightweight symmetric encryption algorithm is used to achieve privacy-preserving. Finally, a credible experimental model on EOS and Aliyun cloud is built. Results show that BacCPSS is feasible and effective, and can achieve secure access in CPSS while protecting privacy.