Distributed multi-agent learning enables agents to cooperatively train a model without requiring to share their datasets. While this setting ensures some level of privacy, it has been shown that, even when data is not directly shared, the training process is vulnerable to privacy attacks including data reconstruction and model inversion attacks. Additionally, malicious agents that train on inverted labels or random data, may arbitrarily weaken the accuracy of the global model. This paper addresses these challenges and presents Privacy-preserving and Accountable Distributed Learning (PA-DL), a fully decentralized framework that relies on Differential Privacy to guarantee strong privacy protection of the agents data, and Ethereum smart contracts to ensure accountability.
Christian Killer, Markus Knecht, Claude Müller, Bruno Rodrigues · 7 authors
Remote Electronic Voting (REV) systems allow voters to cast their votes in an uncontrolled, distributed environment. At the same time, the REV system must provide ballot privacy and verifiability of the final tally. Research has proposed REV schemes offering ballot privacy based on computational intractability assumptions, but only a few provide Unconditional Privacy (UP). Therefore, this work proposes Æternum, a REV system with a voting scheme providing UP. Æternum does not require trust in a central authority, nor does it assume computational intractability of an underlying mathematical problem to provide UP. To satisfy UP's minimal trust assumptions, Æternum uses a permissioned Distributed Ledger (DL), that forms a decentralized network of permissioned nodes, which serve as a transparent, tamper-proof Decentralized Public Bulletin Board (DPBB).
Jan Lauinger, Jens Ernstberger, Emanuel Regnath, Mohammad Hamad · 5 authors
Self-sovereign Identity Management (SSIM) pro-motes self-control of credentials without relying on external administration. However, the state-of-the-art SSIM based on Decentralized Identifiers and Verifiable Credentials (VCs) defined by the World Wide Web Consortium does not enable credential holders to verify whether a Credential Issuing Authority (CIA) legitimately issued a credential.As a remedy, our work constructs a secure authentication protocol, called A-PoA, to provide decentralized and anonymous authorization of CIAs. We leverage a cryptographic accumulator to enable the Root Authority (registering a Credential Schema) with the ability to authorize a CIA (registering a Credential Definition) to issue a credential. The proof of accumulator membership relies on a non-interactive zero-knowledge proof. This allows a credential holder or validator node to verify the validity of a CIA, while the CIA remains anonymous. Our security analysis shows the integrity and confidentiality of our protocol against hostile network participants and our experimental evaluation shows constant verification times independent of the number of authenticated CIAs. Hence, A-PoA introduces the missing building block to develop SSIM-capable and VC-compatible ecosystems acting as a drop-in replacement for traditional Public Key Infrastructure schemes.
Anna Chukhnina, Grigorii Melnikov, Anton Pecherkin, Александр Владимирович Соколов · 5 authors
Many research papers with data processing are released daily. Some of them are prone to statistical errors caused by selective reporting. It results in a lack of reproducibility and wrong conclusions. In the Non-selective reporting demo, we show how to deal with the reporting bias problem through a distributed ledger system.
Blockchain is an incipient technology that offers several advantages over traditional systems, such as decentralization, transparency and traceability. The architecture and characteristics specific to this technology shall, however, have consequences on how personal data is stored and processed. We present a detailed analysis of the European data protection regulation and discuss the weaknesses and strengths of solutions built using blockchain technology regarding the security and privacy requirements established by that regulation. We study the integration of off-chain capabilities in blockchain-based solutions and put forward a methodological framework that can be used to design secure and privacy aware information systems that combine operational off-chain constructs with traditional blockchain functionalities.
Lack of trust is the main barrier preventing more widespread data sharing. The lack of transparent and reliable infrastructure for data sharing prevents many data owners from sharing their data.\nData trust is a paradigm that facilitates data sharing by forcing data controllers to be transparent about the process of sharing and reusing data.\n\nBlockchain technology has the potential to present the essential properties for creating a practical and secure data trust framework by transforming current auditing practices and automatic enforcement of smart contracts logic without relying on intermediaries to establish trust. \nBlockchain holds an enormous potential to remove the barriers of traditional centralized applications and propose a distributed and transparent administration by employing the involved parties to maintain consensus on the ledger. Furthermore, smart contracts are a programmable component that provides blockchain with more flexible and powerful capabilities. Recent advances in blockchain platforms toward smart contracts' development have revealed the possibility of implementing blockchain-based applications in various domains, such as health care, supply chain and digital identity. \n\nThis dissertation investigates the blockchain's potential to present a framework for data trust. It starts with a comprehensive study of smart contracts as the main component of blockchain for developing decentralized data trust. \nInterrelated, three decentralized applications that address data sharing and access control problems in various fields, including healthcare data sharing, business process, and physical access control system, have been developed and examined.\n\nIn addition, a general-purpose application based on an attribute-based access control model is proposed that can provide trusted auditability required for data sharing and access control systems and, ultimately, a data trust framework. Besides auditing, the system presents a transparency level that both access requesters (data users) and resource owners (data controllers) can benefit from. The proposed solutions have been validated through a use case of independent digital libraries. It also provides a detailed performance analysis of the system implementation.\nThe performance results have been compared based on different consensus mechanisms and databases, indicating the system's high throughput and low latency. \n\nFinally, this dissertation presents an end-to-end data trust framework based on blockchain technology. \nThe proposed framework promotes data trustworthiness by assessing input datasets, effectively managing access control, and presenting data provenance and activity monitoring. A trust assessment model that examines the trustworthiness of input data sets and calculates the trust value is presented.\nThe number of transaction validators is defined adaptively with the trust value. \nThis research provides solutions for both data owners and data users’ by ensuring the trustworthiness and quality of the data at origin and transparent and secure usage of the data at the end. A comprehensive experimental study indicates the presented system effectively handles a large number of transactions with low latency.
Smart vehicles are expected to be equipped with high-dimensional, resource-intensive applications, including platoon control, augmented reality supported gaming, AI-based pedestrian detection, fuel scheduling, and so on, catering to diverse user preferences and enhancing safety and efficiency. These applications pose unique challenges for resource-constrained vehicles due to their intense computation requirements, whereas vehicular edge computing (VEC) networks, consisting of roadside units (RSUs) and MEC servers, contain the capability of providing cloud-like computing experience at vehicular edges while meeting performance requirements in terms of latency and throughput. Moreover, the development of intelligent VEC (IVEC) infrastructure is accelerated due to rapid advancement of AI algorithms in recent years. However, IVEC is prone to attacks, including fake computation feedback, unfair or biased resource allocation in a VEC server, and so on, due to its centralized governance and black box computation (edge computation works like a black box for end users). To combat such security vulnerabilities, we propose a blockchain-based decentralized architecture to enhance transparency in IVEC resource management and leverage edge consumers (e.g., vehicles) with a computation verification option. Additionally, we address the unbalanced load distribution issue and propose a secure IVEC federation model for balancing loads. We also outline the main challenges and provide a brief description of promising research directions to draw the attention of concerned stakeholders and parties in both the blockchain and edge computing domains.
Access control is a fundamental keystone in security. Damgard, Haagh, and Orlandi (TCC 2016) introduced access˚ control encryption (ACE) that enforces no-read and no-write rules without revealing the senders, receivers, or the content of the encrypted traffic. Existing designs of ACE for arbitrary policy (covering all possibilities of read/write relationship) rely on indistinguishability obfuscation or lattice-based assumptions, with either exponential-size ciphertexts or circuit realization of policy. Also, their designs mandate a private sanitizer key to remain perpetually online for sanitization. The only existing scheme that can afford a public sanitizer key supports only simple policies. To summarize, state-of-the-art ACE schemes only feature at most two of the following desirable properties: arbitrarypolicy, constant-size (ciphertext), and efficient (sanitization). This paper introduces an ACE scheme for arbitrary policy without sanitizer key, which solves the open question posed by Kim and Wu (Asiacrypt 2017). We also put forth the notion of cross-domain ACE, separating the key generator into the sender-authority and receiver-authority. Our scheme requires structure-preserving signatures, non-interactive zero-knowledge proof, and sanitizable identity-based broadcast encryption as the building blocks. It can be instantiated directly from pairing-based assumptions and features constant ciphertext size. We also prototyped our scheme and demonstrated its practical efficiency.
In this work, we design and implement the first protocol for distributed generation of an RSA modulus that can support thousands of parties and offers security against active corruption of an arbitrary number of parties. In a nutshell, we first design a highly optimized protocol for this scale that is secure against passive corruptions, and then amplify its security to withstand active corruptions using lightweight succinct zero-knowledge proofs. Our protocol achieves security with "identifiable abort," where a corrupted party is identified whenever the protocol aborts, and supports public verifiability.Our protocol against passive corruptions extends the recent work of Chen et al. (CRYPTO 2020) that, in turn, is based on the blueprint introduced in the original work of Boneh-Franklin protocol (CRYPTO 1997, J. ACM, 2001). Specifically, we reduce the task of sampling a modulus to secure distributed multiplication, which we implement via an efficient threshold additively homomorphic encryption scheme based on the Ring-LWE assumption. This results in a protocol where the (amortized) per-party communication cost grows logarithmically in the number of parties. In order to minimize the work done by the parties, we employ a "publicly verifiable" coordinator that is connected to all parties and only performs computations on public data.We implemented both the passive and the active variants of our protocol and ran experiments using 2 to 4,000 parties. This is the first implementation of any MPC protocol that can scale to more than 1,000 parties. For generating a 2048-bit modulus among 1,000 parties, our passive protocol executed in under 6 minutes and the active variant ran in under 25 minutes.
As pass the three revised bills, the Personal Information Protection Act was revised to have a larger application for personal information. For an industrial development through an efficient and secure usage of personal information, there is a need to revise the existing anonymity processing method. This paper modifies the Zero Knowledge Proofs algorithm among the anonymity processing methods to modify the anonymity process calculations by taking into account the reliability of the used service company. More detail, the formula of ZKP (Zero Knowledge Proof) used by ZK-SNAKE is used to modify the personal information for pseudonymization processing. The core function of the proposed algorithm is the addition of user variables and adjustment of the difficulty level according to the reliability of the data user organization and the scope of use. Through Setup_p, the additional variable γ can be selectively applied according to the reliability of the user institution, and the degree of agreement of Witness is adjusted according to the reliability of the institution entered through Prove_p. The difficulty of the verification process is adjusted by considering the reliability of the institution entered through Verify_p. SimProve, a simulator, also refers to the scope of use and the reliability of the input authority. With this suggestion, it is possible to increase reliability and security of anonymity processing and distribution of personal information.
A blockchain-based processing framework for sensitive data is proposed. The smart network comprises a blockchain module, an electronic contract (E-contract) layer node, and a Software-as-a-Service (SaaS) layer module. The underlying blockchain module provides technical support, such as virtual machines, consensus algorithms, transaction verification mechanisms, and accounting mechanisms. The E-contract layer module provides a distributed application service and uses the blockchain technology to support it. In addition, it runs the code of the sensitive data transaction system and the code created by the algorithm for encoding generation at the E-contract layer. The SaaS layer module offers a cloud-platform service that allows each party to easily engage in business collaboration via web portals or clients. The proposed smart system is used by each party get involved in the production of sensitive data. The final sensitive data are produced by the final data generator, and other modules involved in the process of data production are unaware of the final data. This approach prevents the leakage of sensitive data into the circulation.
The vehicular networks constructed by interconnected vehicles and transportation infrastructure are vulnerable to cyber-intrusions due to the expanded use of software and the introduction of wireless interfaces. Intrusion detection systems (IDSs) can be customized efficiently in response to this increased attack surface. There has been significant progress in detecting malicious attack traffic using machine learning approaches. However, existing IDSs require network devices with powerful computing capabilities to continuously train and update complex network models, which reduces the efficiency and defense capability of intrusion detection systems due to limited resources and untimely model updates. This work proposes a cooperative intrusion detection mechanism that offloads the training model to distributed edge devices (e.g., connected vehicles and roadside units (RSUs). Distributed federated-based approach reduces resource utilization of the central server while assuring security and privacy. To ensure the security of the aggregation model, blockchain is used for the storage and sharing of the training models. This work analyzes common attacks and shows that the proposed scheme achieves cooperative privacy-preservation for vehicles while reducing communication overhead and computation cost.
The act of maintaining educational records both online and on paper have become a norm. With the enforcement of lockdowns due to the Covid-19 pandemic, the education sector attempted to move their entire operations online. However with this move, various operations such as Verification of Documents, Approval of LORs have become harder to deal with in a legitimate manner. In this paper, we have compared various existing methods to deal with the problem at hand and proposed our system for the same.
Abstract Due to the rapid growth of Internet, E-commerce and Internet of Things, people use Web based services for most of their needs including buying items, reading books, watching online shows etc. Several companies are using recommender systems to influence people’s choices based on their likings, behaviours etc. Hence, people fear that their privacy is violated. Also, some of the online applications are not safe and secure. One way to overcome the privacy related issues is using secured solutions such as incorporating blockchain technologies for privacy-based applications. The decentralized nature of blockchain technologies have resolved several security, and authentication problems of Internet of Things systems. In this paper, we conduct a comprehensive survey on the privacy solutions for recommender systems emphasising current status, classification and open issues. We also discuss blockchain technology, including its structure as well as applications of blockchain technology for privacy solutions of recommender systems. Furthermore, we discuss the limitations and delve into future trends that blockchain technology can be adapted for privacy-base applications in the years to come.
In the field of modern bidding, electronic bidding leads a new trend of development, convenience and efficiency and other significant advantages effectively promote the reform and innovation of China’s bidding field. Nowadays, most systems require a strong and trusted third party to guarantee the integrity and security of the system. However, with the development of blockchain technology and the rise of privacy protection, researchers has begun to emphasize the core concept of decentralization. This paper introduces a decentralized electronic bidding system based on blockchain and smart contract. The system uses blockchain to replace the traditional database and uses chaincode to process business logic. In data interaction, encryption techniques such as zero-knowledge proof based on graph isomorphism are used to improve privacy protection, which improves the anonymity of participants, the privacy of data transmission, and the traceability and verifiable of data. Compared with other electronic bidding systems, this system is more secure and efficient, and has the nature of anonymous operation, which fully protects the privacy information in the bidding process.
Shuo Yuan, Bin Cao, Yao Sun, Zhiguo Wan · 5 authors
Introducing blockchain into Federated Learning (FL) to build a trusted edge computing environment for transmission and learning has attracted widespread attention as a new decentralized learning pattern. However, traditional consensus mechanisms and architectures of blockchain systems face significant challenges in handling large-scale FL tasks, especially on Internet of Things (IoT) devices, due to their substantial resource consumption, limited transaction throughput, and complex communication requirements. To address these challenges, this paper proposes ChainFL, a novel two-layer blockchain-driven FL system. It splits the IoT network into multiple shards within the subchain layer, effectively reducing the scale of information exchange, and employs a Direct Acyclic Graph (DAG)-based mainchain as the mainchain layer, enabling parallel and asynchronous cross-shard validation. Furthermore, the FL procedure is customized to integrate deeply with blockchain technology, and a modified DAG consensus mechanism is designed to mitigate distortion caused by abnormal models. To provide a proof-of-concept implementation and evaluation, multiple subchains based on Hyperledger Fabric and a self-developed DAG-based mainchain are deployed. Extensive experiments demonstrate that ChainFL significantly surpasses conventional FL systems, showing up to a 14% improvement in training efficiency and a threefold increase in robustness.
Muhammad Habib ur Rehman, Ahmed Mukhtar Dirir, Khaled Salah, Ernesto Damiani · 5 authors
Cross-device federated learning (CDFL) systems enable fully decentralized training networks whereby each participating device can act as a model-owner and a model-producer. CDFL systems need to ensure fairness, trustworthiness, and high-quality model availability across all the participants in the underlying training networks. This article presents a blockchain-based framework, TrustFed, for CDFL systems to detect the model poisoning attacks, enable fair training settings, and maintain the participating devices' reputation. TrustFed provides fairness by detecting and removing the attackers from the training distributions. It uses blockchain smart contracts to maintain participating devices' reputations to compel the participants in bringing active and honest model contributions. We implemented the TrustFed using a Python-simulated federated learning framework, blockchain smart contracts, and statistical outlier detection techniques. We tested it over the large-scale industrial Internet of things dataset and multiple attack models. We found that TrustFed produces better results regarding multiple aspects compared with the conventional baseline approaches.
Digital evidence, such as evidence from CCTV and event data recorders, is highly valuable in criminal investigations, and is used as definitive evidence in trials. However, there are risks when digital evidence obtained during the investigation of a case is managed through a physical hard disk drive until it is submitted to the court. Previous studies have focused on the integrated management of digital evidence in a centralized system, but if a centralized system server is attacked, major operations and investigation information may be leaked. Therefore, there is a need to reliably manage digital evidence and investigation information using blockchain technology in a distributed system environment. However, when large amounts of data-such as evidence videos-are stored in a blockchain, the data that must be processed only within one block before being created increase, causing performance degradation. Therefore, we propose a two-level blockchain system that separates digital evidence into hot and cold blockchains. In the criminal investigation process, information that frequently changes is stored in the hot blockchain, and unchanging data such as videos are stored in the cold blockchain. To evaluate the system, we measured the storage and inquiry processing performance of digital crime evidence videos according to the different capacities in the two-level blockchain system.
Safa Otoum, Ismaeel Al Ridhawi, Hussein T. Mouftah
The COVID-19 pandemic, which spread rapidly in late 2019, has revealed that the use of computing and communication technologies provides significant aid in preventing, controlling, and combating infectious diseases. With the ongoing research in next-generation networking (NGN), the use of secure and reliable communication and networking is of utmost importance when dealing with users' health records and other sensitive information. Through the adaptation of Artificial Intelligence (AI)-enabled NGN, the shape of healthcare systems can be altered to achieve smart and secure healthcare capable of coping with epidemics that may emerge at any given moment. In this article, we envision a cooperative and distributed healthcare framework that relies on state-of-the-art computing, communication, and intelligence capabilities, namely, Federated Learning (FL), mobile edge computing (MEC), and Blockchain, to enable epidemic (or suspicious infectious disease) discovery, remote monitoring, and fast health-authority response. The introduced framework can also enable secure medical data exchange at the edge and between different health entities. Such a technique, coupled with the low latency and high bandwidth functionality of 5G and beyond networks, would enable mass surveillance, monitoring and analysis to occur at the edge. Challenges, issues, and design guidelines are also discussed in this article with highlights on some trending solutions.
Alexandre Siqueira, Arlindo Flavio Da Conceição, Vladimir Rocha
Self-Sovereign Identity (SSI), a Blockchain-based technology for digital\nidentity management, is a promising concept for handling health data. It could\nrepresent a step forward in empowering users, granting them control over their\ndata. This work conducts a systematic literature review to investigate\nstate-of-the-art measures based on SSI and Blockchain technologies for dealing\nwith electronic health records (EHRs), identifying gaps, and determining the\nkey questions for future research. As a result, this review shows a growing\ninterest in Blockchain methods to handle EHRs, but few works consider using the\nself-sovereign identity approaches. The results obtained in this work also\nsuggest that: Blockchain technologies provide a viable alternative to deliver\nEHR solutions such as patient monitoring, healthcare data trading, and\nprescription control; consolidated Blockchain technologies are the preferred\ncore components of most effective strategies; keeping raw health data off-chain\nhelps to create scalable solutions; health data standards make searching\nmedical records in Blockchain structures feasible; Smart Contracts are\nessential components of Blockchain-based EHR solutions; the concepts of data\nownership and Self-Sovereign Identity have been neither adequately defined nor\nemployed in the health context.\n
As autonomous vehicles fill the roads and more manufacturers join the trend, the need for a unified communication protocol grows. Current paradigms in vehicle-to-vehicle communication are too slow to provide accurate and meaningful traffic data in a timely fashion, and it is difficult to trust that incoming data is correct without an authoritative server verifying the sender's identity. This paper introduces a protocol for peer-to-peer exchanges of positional data that determines the trust level of a particular message by comparing matching object data hashes. Similar in concept to non-interactive zero-knowledge proofs, the design retains the privacy and anonymity of senders and is relatively fast compared to certificate-based solutions under a reasonable traffic load. Our preliminary experiment shows promising results, with much faster runtimes compared to similar cryptographic solutions. Although the current implementation is still rough around the edges, the basic design can provide the groundwork for future paradigms in inter-vehicular communication without depending on expensive cryptographic operations performed on special or more powerful hardware. This opens doors for protocols that can be run on current vehicles without requiring the collective processing power of all vehicles to increase.