Searchable encryption enables users to enjoy search services while protecting the security and privacy of their outsourced data. Blockchain-enabled searchable encryption delivers the computing processes that are executed on the server to the decentralized and transparent blockchain system, which eliminates the potential threat of malicious servers invading data. Recently, although some of the blockchain-enabled searchable encryption schemes realized that users can search freely and verify search results, unfortunately, these schemes were inefficient and costly. Motivated by this, we proposed an improved scheme that supports fine-grained access control and flexible searchable encryption. In our framework, the data owner uploads ciphertext documents and symmetric keys to cloud database and optional KMS, respectively, and manipulates the access control process and searchable encryption process through smart contracts. Finally, the experimental comparison conducted on a private Ethereum network proved the superiority of our scheme.
Recently, blockchain-based federated learning (BFL) has attracted intensive research attention due to that the training process is auditable and the architecture is serverless avoiding the single point failure of the parameter server in vanilla federated learning (VFL). Nevertheless, BFL tremendously escalates the communication traffic volume because all local model updates (i.e., changes of model parameters) obtained by BFL clients will be transmitted to all miners for verification and to all clients for aggregation. In contrast, the parameter server and clients in VFL only retain aggregated model updates. Consequently, the huge communication traffic in BFL will inevitably impair the training efficiency and hinder the deployment of BFL in reality. To improve the practicality of BFL, we are among the first to propose a fast blockchain-based communication-efficient federated learning framework by compressing communications in BFL, called BCFL. Meanwhile, we derive the convergence rate of BCFL with non-convex loss. To maximize the final model accuracy, we further formulate the problem to minimize the training loss of the convergence rate subject to a limited training time with respect to the compression rate and the block generation rate, which is a bi-convex optimization problem and can be efficiently solved. To the end, to demonstrate the efficiency of BCFL, we carry out extensive experiments with standard CIFAR-10 and FEMNIST datasets. Our experimental results not only verify the correctness of our analysis, but also manifest that BCFL can remarkably reduce the communication traffic by 95-98% or shorten the training time by 90-95% compared with BFL.
Privacy preservation in various contact tracing approaches for the COVID-19 or SARS-CoV-2 virus is challenging, as such applications tend to reveal usersâ points of interest (POIs) and other sensitive data shared together with their location information. This paper proposes COVID-19 eavesdropping resistant tracing (COVERT)-Blockchain, a novel distributed-ledger-based platform to facilitate contact tracing without invading usersâ privacy. COVERT-Blockchain enables infected users to share only their anonymized location traces on the Blockchain with a sliding window of the previous 15 days, thereby avoiding constant location information sharing with third party users. To further reduce the chances of revealing the corresponding usersâ trajectories, in COVERT-Blockchain we employ an adaptive logging mechanism to store trajectory data for contact tracing only if the users stayed in a location where there is significant presence of other humans around them for a relatively long duration of time. This ensures anonymity where the trajectory is generated differently each time for each user, and such infrequent and random trajectory generation enables us to generate unidentifiable trajectories for each user and thus preserve their privacy. COVERT-Blockchain is evaluated for scalability and robustness in relation to overhead and delays in storing and retrieving data from the Blockchain. Results show it to efficiently achieve contact tracing without any breaches of privacy.
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 âVRepChainâ 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.
In order to protect data privacy in the context of big data, a new scheme to protect data privacy and user privacy is proposed. This study believes that blockchain technology can be used to build a massive private data retrieval and sharing platform, considering the realization of privacy protection under horizontal federated learning and vertical federated learning, combined with privacy computing technology, to verify the results of massive data retrieval, and to build a "data availability that is not available." The "visible" security reduction model involves three cryptographic algorithms, namely functional encryption, zero-knowledge proof and asymmetric encryption. By combining blockchain and cloud servers, a hybrid storage architecture with data storage on the chain and off-chain storage is realized. And use function encryption and zero-knowledge proof to achieve privacy protection and secure data sharing of verifiable results. The final experimental results prove that the security feasibility of this model is proposed in this paper, which can fully improve the level of data log security management, operation and maintenance supervision, and the quality and efficiency of data security protection, and improve the network security protection system of the power grid in all scenarios.
Abeer AlSereidi, Sarah Qahtan, R. T. Mohammed, A. A. Zaidan ¡ 17 authors
Context: When the epidemic first broke out, no specific treatment was available for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The urgent need to end this unusual situation has resulted in many attempts to deal with SARS-CoV-2. In addition to several types of vaccinations that have been created, anti-SARS-CoV-2 monoclonal antibodies (mAbs) have added a new dimension to preventative and treatment efforts. This therapy also helps prevent severe symptoms for those at a high risk. Therefore, this is one of the most promising treatments for mild to moderate SARS-CoV-2 cases. However, the availability of anti-SARS-CoV-2 mAb therapy is limited and leads to two main challenges. The first is the privacy challenge of selecting eligible patients from the distribution hospital networking, which requires data sharing, and the second is the prioritization of all eligible patients amongst the distribution hospitals according to dose availability. To our knowledge, no research combined the federated fundamental approach with multicriteria decision-making methods for the treatment of SARS-COV-2, indicating a research gap. Objective: This paper presents a unique sequence processing methodology that distributes anti-SARS-CoV-2 mAbs to eligible high-risk patients with SARS-CoV-2 based on medical requirements by using a novel federated decision-making distributor. Method: This paper proposes a novel federated decision-making distributor (FDMD) of anti-SARS-CoV-2 mAbs for eligible high-risk patients. FDMD is implemented on augmented data of 49,152 cases of patients with SARS-CoV-2 with mild and moderate symptoms. For proof of concept, three hospitals with 16 patients each are enrolled. The proposed FDMD is constructed from the two sides of claim sequencing: central federated server (CFS) and local machine (LM). The CFS includes five sequential phases synchronised with the LMs, namely, the preliminary criteria setting phase that determines the high-risk criteria, calculates their weights using the newly formulated interval-valued spherical fuzzy and hesitant 2-tuple fuzzy-weighted zero-inconsistency (IVSH2-FWZIC), and allocates their values. The subsequent phases are federation, dose availability confirmation, global prioritization of eligible patients and alerting the hospitals with the patients most eligible for receiving the anti-SARS-CoV-2 mAbs according to dose availability. The LM independently performs all local prioritization processes without sharing patientsâ data using the provided criteria settings and federated parameters from the CFS via the proposed Federated TOPSIS (F-TOPSIS). The sequential processing steps are coherently performed at both sides. Results and Discussion: (1) The proposed FDMD efficiently and independently identifies the high-risk patients most eligible for receiving anti-SARS-CoV-2 mAbs at each local distribution hospital. The final decision at the CFS relies on the indexed patientsâ score and dose availability without sharing the patientsâ data. (2) The IVSH2-FWZIC effectively weighs the high-risk criteria of patients with SARS-CoV-2. (3) The local and global prioritization ranks of the F-TOPSIS for eligible patients are subjected to a systematic ranking validated by high correlation results across nine scenarios by altering the weights of the criteria. (4) A comparative analysis of the experimental results with a prior study confirms the effectiveness of the proposed FDMD. Conclusion: The proposed FDMD has the benefits of centrally distributing anti-SARS-CoV-2 mAbs to high-risk patients prioritized based on their eligibility and dose availability, and simultaneously protecting their privacy and offering an effective cure to prevent progression to severe SARS-CoV-2 hospitalization or death.
In this paper, we propose a blockchain-based collaborative credential management scheme for anonymous authentication in space-air-ground integrated vehicular networks (SAGVN), namedSAG-BC. First, we build a consortium blockchain among service providers and design a distributed system setup (DSS) scheme to securely generate public parameters for issuing credentials. Second, we design a collaborative credential issuance (CCI) scheme to generate a succinct and easy-to-manage subscription credential. The credential can be used by users to access different access points in SAGVN efficiently without revealing true identities from the authentication messages. With co-designs of zero-knowledge proofs and succinct on-chain commitments,SAG-BCprovides efficient verifiability and incentives for credential management operations in SAGVN. By doing so, expensive on-chain storage and computational overheads are reduced in the DSS and CCI. Finally, we conduct a thorough security analysis to demonstrate thatSAG-BCachieves security and verifiability for credential management in SAGVN. We set up a real-world blockchain network and conduct extensive experiments to show the feasibility and efficiency ofSAG-BC.
Mirko Zichichi, Stefano Ferretti, Gabriele DâAngelo, Victor RodrÄąĚguez-Doncel
Abstract The centralization of control over the processing of personal data threatens the privacy of individuals due to the lack of transparency and the obstruction of easy access to their data. Individuals need the tools to effectively exercise their rights, enshrined in regulations such as the European Union General Data Protection Regulation (GDPR). Having direct control over the flow of their personal data would not only favor their privacy but also a âdata altruismâ, as supported by the new European proposal for a Data Governance Act. In this work, we propose a multi-layered architecture for the management of personal information based on the use of distributed ledger technologies (DLTs). After an in-depth analysis of the tensions between the GDPR and DLTs, we propose the following components: (1) a personal data storage based on a (possibly decentralized) file storage (DFS) to guarantee data sovereignty to individuals, confidentiality and data portability; (2) a DLT-based authorization system to control access to data through two distributed mechanisms, i.e. secret sharing (SS) and threshold proxy re-encryption (TPRE); (3) an audit system based on a second DLT. Furthermore, we provide a prototype implementation built upon an Ethereum private blockchain, InterPlanetary File System (IPFS) and Sia and we evaluate its performance in terms of response time.
Almost all data collected today is in digital form and is stored in various places ranging from local databases to the cloud-based storage. However, all this data is useful only when collected and aggregated for further processing and analysis. As such, data sharing among various sources has now become very important. Openly sharing data tends to violate confidentiality and privacy of users. Due to this, sharing of data employs a number of access control methods to ensure that only the authorized and authenticated users are getting access. Most access control methods however, are controlled by a single centralized entity, responsible for writing and enforcing the access policies. Such a system faces the issues as any centralized system includes single point of failure and malicious behavior. With the rising presence of a lot of sensitive and personal-related data such as in healthcare and the Internet of Things (IoT), it has become important to enable fine-grained access control that is in the hands of the owners of the data rather than centralized personnel. It has also become important to ensure that the system is fault-tolerant and secure from attacks as a single breach of data could cause a great loss of sensitive data. Along with this, it is necessary for systems to be able to record access trails for higher security purposes. Blockchain is a public distributed ledger technology that has proven to be tamper-proof and does not require any trusted third party for its usage. With the development of smart contracts on the blockchain, enabling the automatic execution of turing-complete code on the the blockchain network, it has grown to a wide number of applications with one of them being to provide secure and privacy-preserving access control methods. This paper provides a survey of the various access control methods that have been developed in the recent years using blockchain. A study considering the usage of blockchain, smart contracts, methods proposed, and improvised as well as limitations if any of the various papers has been performed and depicted in tabular and descriptive manner. This paper aims to guide readers to gain an in-depth understanding of current research in this field as well as gain insights on future prospects.
Weiqi Dai, Shuyue Tuo, Liang Yu, KimâKwang Raymond Choo ¡ 6 authors
Data is a key asset in our interconnected and smart city. Especially, in the context of healthcare, healthcare data can facilitate remote diagnosis and medical research. Because of the potentially sensitive nature of healthcare data, privacy is a key consideration for both individuals and organizations. We can broadly categorize privacy considerations into data privacy, attribute privacy, and privilege policy privacy. To support one or more notions of privacy, the potential of solutions, such as fine-grained access control [e.g., those based on attribute-based encryption (ABE)] and blockchain in realizing data sharing has been explored. However, these approaches generally only facilitate access control of data and the traceability of the sharing process, and do not protect the attribute and privilege policy privacy of users. Therefore, in this article, we implement HAPPS, a hidden attribute and privilege-protection data-sharing scheme with verifiability. The three key building blocks of HAPPS are zero-knowledge proof, blockchain, and distributed ABE (DABE). Specifically, in our approach, we propose a new data access control strategy (i.e., attribute-hidden zero-knowledge proofâat-ZKP) to hide user identity and attributes during the authorization process. Our scheme is embedded in the blockchain and built into the decentralized sharing platform to prevent central verifier counterfeiting and support auditing. To demonstrate utility, we prove that HAPPS ensures data, attribute, and privilege policy privacy. Findings of our evaluations implemented on Ethereum and using the data set from the healthcare cost and utilization project (HCUP), we demonstrate that our scheme can share sensitive healthcare records belonging to minors (e.g., children) without the at-ZKP incurring unrealistic cost.
G. Yedukondalu, Channapragada Rama Seshagiri Rao, Raman Dugyala
Industrial Internet of Things (IIoT) is changing many driving enterprises like transportation, mining, horticulture, energy and medical care. Machine Learning calculations are utilized for getting stages for IT frameworks. The IoT network unit hubs typically asset in a strange manner by making them more responsible to digital assaults. IIoT frameworks requests various situations in genuine one among them is giving security and the causes that encompass them in true viewpoints. It incorporates a system called PriModChain causes security and reliability on IIoT information by joining differential protection, Ethereum block chain and unified Machine learning. Consequently, security will be compromised and we use PriMod chain for giving protection and different compliances and created utilizing Python with attachment programming on essential PC.
Smart contract over the blockchain accelerates the deployment of decentralized Industrial Internet of Things (IIoT) applications. However, the usability of smart contracts is constrained to the data on the blockchain, and has no direct access to the off-chain environment. Consequently, smart contract cannot directly serve as a carrier for the IIoT applications that rely on intensive data exchange and task computation. To cope with this issue, we first propose an oracle-aided IIoT blockchain (OIB) system to facilitate the deployment of smart contract based IIoT applications. Specifically, a distributed oracle network is employed to not only bridge the off-chain environment and smart contracts, but also extend the computing capability of smart contracts. Then, we study important properties of the OIB system, that is, liveness, safety, and fairness. In particular, we propose a new challenge of fairness to guarantee that each oracle can reap positive utility (i.e., individual rationality), and a new challenge of liveness to meet delay constraints of oracle services (i.e., delay-sensitivity). To address these two challenges, we develop an auction based incentive mechanism for the data-feeding oracles to achieve individual rationality as well as incentive compatibility, and a delay-sensitive matching based incentive mechanism for the computing oracles under specific delay constraints. Simulation results corroborate that the proposed auction based incentive mechanism guarantees not only truthfulness but also individual rationality, and show that the matching based incentive mechanism achieves smaller delay than the random selection scheme used in the Decentralized Oracle Service (DOS) network.
James E. Short, Ken Miyachi, Christian D. Toouli, Steve Todd
The rapid upswing in interest in federated learning (FL) and federated analytics (FA) architectures has corresponded with the rapid increase in commercial AI software products, ranging from face detection and language translation to connected IOT devices, smartphones, and autonomous vehicles equipped with high-resolution sensors. However, the traditional client-server model does not readily address questions of data ownership, privacy, and data location in the context of the multiple datasets required for machine learning. In this paper, we report on a pilot distributed ledger and smart contract network model, designed to track analytic jobs in an HPC supercomputing environment. The test system design integrates the FL/FA model into a blockchain-based network architecture, wherein the test system records interactions with the global server and blockchain network. The design goal is to create a secure audit trail of supercomputer analytic operations and the ability to securely federate those operations across multiple supercomputer deployments. As there are still relatively few real-world applications of FL/FA models and blockchain networks in use, our system design, test deployment, and sample code are intended to provide interested researchers with exploratory tools for future research.
Yuzheng Ren, Renchao Xie, F. Richard Yu, Tao Huang ¡ 5 authors
Recently, the Internet of vehicles (IoV) and connected and autonomous vehicles (CAVs) have become research hotspots. The accuracy and efficiency of artificial intelligence (AI) models are crucial for CAVs to make decisions automatically. Intelligence networking enables each CAV to train appropriate models locally with the help of other CAVs' intelligence and make up for the lack of experience and computing power of a single-vehicle. However, intelligence is distributed across diverse geo-locations in the intelligence networking paradigm, which calls for accurate, efficient, and lightweight intelligence discovery mechanisms. In this article, we propose a non-fungible token (NFT)-based distributed intelligence networking scheme (NDIN) for CAVs. We use NFT to tokenize intelligence and efficiently describe intelligence from multiple aspects through meta-data, facilitating applications to better understand and search for intelligence in complex and trust-lacking IoV. We present the architecture, modules, and core mechanism of NDIN. Then, we formulate the essential problem as a discrete Markov decision process (MDP) and adopt the quantum-inspired reinforcement learning (QRL) algorithm to find the optimal policy. Also, the convergence rate and performance compared with existing schemes are evaluated. Finally, we discuss several related challenges and opportunities.
Digital Twin (DT) supports real time analysis and provides a reliable simulation platform in the Internet of Things (IoT). The creation and application of DT hinges on amounts of data, which poses pressure on the application of Artificial Intelligence (AI) for DT descriptions and intelligent decision-making. Federated Learning (FL) is a cutting-edge technology that enables geographically dispersed devices to collaboratively train a shared global model locally rather than relying on a data center to perform model training. Therefore, DT can benefit by combining with FL, successfully solving the âdata islandâ problem in traditional AI. However, FL still faces serious challenges, such as enduring single-point failures, suffering from poison attacks, lacking effective incentive mechanisms. Before the successful deployment of DT, we should tackle the issues caused by FL. Researchers from industry and academia have recognized the potential of introducing Blockchain Technology (BT) into FL to overcome the challenges faced by FL, where BT acting as a distributed and immutable ledger, can store data in a secure, traceable, and trusted manner. However, to the best of our knowledge, a comprehensive literature review on this topic is still missing. In this paper, we review existing works about blockchain-enabled FL and visualize their prospects with DT. To this end, we first propose evaluation requirements with respect to security, fault-tolerance, fairness, efficiency, cost-saving, profitability, and support for heterogeneity. Then, we classify existing literature according to the functionalities of BT in FL and analyze their advantages and disadvantages based on the proposed evaluation requirements. Finally, we discuss open problems in the existing literature and the future of DT supported by blockchain-enabled FL, based on which we further propose some directions for future research.
Xi Peng, Xinglong Zhang, Lian Wang, Wenjuan Liu ¡ 5 authors
Medical data contains multiple records of patient data that are important for subsequent treatment and future research. However, it needs to be stored and shared securely to protect the privacy of the data. Blockchain is widely used in the management of healthcare data because of its decentralized and tamper-proof features. In order to study the development of blockchain in healthcare, this paper evaluates it from various perspectives. We analyze blockchain-based approaches from different application scenarios. These are blockchain-based electronic medical record sharing, blockchain and the Internet of Medical Things and blockchain-based federal learning. The results show that blockchain and smart contracts have a natural advantage in the field of medical data since they are tamper-proof and traceable. Finally, the challenges and future directions of blockchain in healthcare are discussed, which can help drive the field forward.
Searchable encryption (SE) has emerged as a cryptographic primitive that allows data users to search on encrypted data. Most existing SE schemes usually delegate search operations to an intermediary such as a cloud server, which would inevitably result in single-point failure, privacy leakage, and even untrustworthy results. Several blockchain-based SE schemes have been proposed to alleviate these issues; however, they suffer from some issues, such as the support for multi-keyword multi-owner model, query privacy and data storage availability. In this paper, we propose BPMS, blockchain-based privacy-preserving multi-keyword search in multi-owner setting, which supports searching over encrypted data in trustworthy, private and efficient manners. The attribute Bloom filter has been introduced into our BPMS to build indexes, which protects query privacy and improves index generation performance. To guarantee data storage availability, our BPMS leverages the advantages of IPFS (InterPlanetary File System) to store large scale of encrypted data. Security proof and comparative analysis in theory indicate that our BPMS is more secure and efficient. A series of experiments conducted on a real-world dataset further demonstrate that our BPMS is feasible in practice.
Junchuan Lianga, Rong Wang, C. Feng, ChinâChen Chang
<title>Abstract</title> As one kind of distributed machine learning technique, federated learning enables multiple clients to build a model across decentralized datacollaboratively without explicitly aggregating the data. Due to its abilityto break data silos, federated learning has received increasing attentionin many fields, including finance, healthcare, and education. However,the invisibility of clientsâ training data and the local training process result in some security issues. Recently, many works have beenproposed to research the security attacks and defenses in federatedlearning, but there has been no special survey on poisoning attacks onfederated learning and the corresponding defenses. In this paper, weinvestigate the most advanced schemes on federated learning poisoningattacks and defenses and point out the future directions in these areas.
Wei Liang, Yang Yang, Ce Yang, Yonghua Hu ¡ 7 authors
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.
As globalization takes place, multiple data resources need to be integrated, and the degree of information flow between regions determines the degree of integration development between regions. Data sharing is even more sensitive when it comes to national and individual interests and is a matter of national security and national interest. Although Privacy-Preserving Computing achieves privacy protection for input data in the process of multi-party collaborative computing. However, the original data, computing process, and results face verification problems. As a result, sharing data on the blockchain will leak private information. To ensure the security of personal privacy information and recording in monitoring systems, This paper studies the use of homomorphic encryption for the secure transfer of blockchain data. Using our solution, the problem of data leakage in data sharing can be addressed by assuming that the data cannot be tampered with. This method has high transmission accuracy and a short transmission time, which effectively prevents data tampering caused by long transmission times.
The evolution of smart contracts in recent years inspired a crucial question: do smart contract evaluation protocols provide the required level of privacy when executing contracts on the blockchain? The Hawk (IEEE S&P â16) paper introduces a way to solve the problem of privacy in smart contracts by evaluating the contracts off-chain, albeit with the trust assumption of a manager. To avoid the partially trusted manager altogether, a novel approach named zkHawk (IEEE BRAINS â21) explains how we can evaluate the contracts privately off-chain using a multi-party computation (MPC) protocol instead of trusting said manager. This paper dives deeper into the detailed construction of a variant of the zkHawk protocol titled V-zkHawk using formal proofs to construct the said protocol and model its security in the universal composability (UC) framework (FOCS â01). The V-zkHawk protocol discussed here does not support immediate closure, i.e., all the parties (n) have to send a message to inform the blockchain that the contract has been executed with corruption allowed for up to t parties, where t<n. In the most quintessential sense, the V-zkHawk is a variant because the outcome of the protocol is similar (i.e., execution of smart contract via an MPC function evaluation) to zkHawk, but we modify key aspects of the protocol, essentially creating a small trade-off (removing immediate closure) to provide UC (stronger) security. The V-zkHawk protocol leverages joint Schnorr signature schemes, encryption schemes, Non-Interactive Zero-Knowledge Proofs (NIZKs), and commitment schemes with Common Reference String (CRS) assumptions, MPC function evaluations, and assumes the existence of asynchronous, authenticated broadcast channels. We achieve malicious security in a dishonest majority setting in the UC framework.
The decentralization of digital identity management brought notable advantages to the user, together with complete ownership of his identity. In this paper is summarized the state of the art of the Identity and Access Management in the blockchain environment and the related regulatory or guidelines with a proposal, based on literature and on the previous experiences.
Dhaval Thummar, Yerramaddu Jahnavi, M R Prathyusha, Sayad Shahanaz ¡ 6 authors
The admission process in academic institutions (universities, colleges, etc.) is more digitized than ever. Starting from standardized tests to application processing, to shortlisting on the basis of merit, to even document verification, everything is carried out through online processes now. However, in spite of having huge benefits in terms of convenience, existing admission processes severely lack transparency. The entire process is dependent on certain central authoritative entities such as the testing authorities followed by the institutes themselves. Moreover, critical tasks such as verifying educational and identity-related documents of students is a tedious affair and the effort is duplicated across all institutions. In this work, we attempt to overcome these limitations of the existing workflow of academic institutes' admission process by designing a distributed ledger based framework that involves the academic institutes, testing authorities, document and credential validators, as well as the students. Our framework DeSAT uses verifiable credentials together with a permissioned ledger to remove the duplicate efforts in verification of test scores as well as validation of students' documents. In addition, it makes the entire process transparent and auditable while enforcing fair merit-based seat allotment through smart contracts. Through a prototype implementation using Hyperledger Fabric, Indy, and Aries, we demonstrate the practicality of DeSAT and show that our system offers acceptable performance while scaling with the number of participating institutions.