This paper describes techniques to help with COVID-19 automated contact tracing, and with the restoration efforts. We describe a decentralized protocol for ``proof-of-contact'' in zero knowledge where a person can publish a short cryptographic proof attesting to the fact that they have been infected and that they have come in contact with a set of people without revealing any information about any of the people involved. More importantly, we describe how to compose these proofs to support broader functionality such as proofs of $n$th-order exposure which can further speed up automated contact tracing. The cryptographic proofs are publicly verifiable, and places the burden on the person proving contact and not on third parties or healthcare providers rendering the system more decentralized, and accordingly more scalable.
Paulo Sergio Rangel Garcia, João Henrique Kleinschmidt
Increased longevity and people's concern about aging with quality has led to increased health and wellness data. Much of the data is not interoperable because of its divergent structures and semantics, being little used and little protected. Existing standards are complex and lack the adherence of the agents involved to ensure their application. Blockchain technology offers alternatives to unify the standards and their application by consensus algorithm, which considers the validation and secrecy in the insertion of the blocks of transactions in the chain. However, smart contracts can ensure secrecy and the rules of data sharing in blocks in the chain. In this paper, we propose a blockchain architecture with a consensus algorithm that considers data collected in the health and wellness ecosystem, including those obtained by IoT devices and persisted in middleware platforms. It is intended that this architecture be able to answer the questions and establish the concepts for the full and secure sharing of health and wellness data.
Due to the rapid development of the internet of Things (ioT), a massive number of devices are connected to the internet. For these distributed devices in ioT networks, how to ensure their security and privacy becomes a significant challenge. Blockchain technology provides a promising solution to protect the data integrity, provenance, privacy, and consistency for ioT networks. in blockchains, communication is a prerequisite for participants, which are distributed in the system, to reach consensus. However, in ioT networks, most of the devices communicate through wireless links, which are not always reliable. Hence, the communication reliability of ioT devices influences the system security. in this article, we rethink the roles of communication and computing in blockchains by accounting for communication reliability. We analyze the trade-off between communication reliability and computing power in blockchain security, and present a lower bound to the computing power that is needed to conduct an attack with a given communication reliability. Simulation results show that adversarial nodes can succeed in tampering with a block with less computing power by hindering the propagation of blocks from other nodes.
Meerja vali Shaik, Ch. Rupa, Rohith Gadde, M N S Koundinya · 5 authors
Nowadays everything seems to be original and it's being herculean task to identify which is not real. It may be a currency for people or valued currency for students such as certificates. A lot of fraudulent parties have made money by encouraging the duplicate certificates in society. As a result, low talented or inefficient people are getting more and they are being responsible for the degradation of any nation’s value. A blockchain-based certificate is a prime solution for the above problem. In this work, would like to discuss the functioning of a smart contract in favor of Issuing, verifying, and revoking the certificates through gas value[9] deductions. Thus our certificate can provide the added assurances of evidence of origin in a transparent manner using.
Yao Yu, Shumei Liu, Lei Guo, Phee Lep Yeoh · 6 authors
Mobile crowdsourcing is a promising strategy for trusted data collection in Internet-of-Things (IoT) applications. In this article, we propose a new fog-blockchain distributed approach for crowdsourcing reputation management to prevent user's privacy leakage, malicious users' participation, and reputation tampering in wireless IoT systems. To protect the user's privacy, we design a cross-layer privacy protection model to separate the user's identity and tasks flexibly by means of a hierarchical structure based on fog computing. Moreover, considering the multiconstraint requirement of crowdsourcing tasks, we present a multifactor reputation evaluation method to accurately identify malicious users. Furthermore, to solve the multi-identity problem of users on multiple fog nodes, we propose an adaptive fog-blockchain reputation storage method, which efficiently reduces the system resource consumption by analyzing the adaptive classification of fog nodes. Exhaustive experimental simulation results validate the security and efficiency of our proposed reputation management system.
Mobile edge computing (MEC) raises the issue of resisting selfish edge attackers that use less computation resources than promised to process offloading tasks or provide faked computation results. In this paper, we present a blockchain based trust mechanism to help MEC address selfish edge attacks and faked service record attacks. This mechanism evaluates the computational performance of the edge devices and broadcasts such information to the neighboring edge devices and mobile devices. By building a reputation assignment method for the edge devices, the edge reputation system chooses the miner of the blockchain, which applies the joint Proof-of-Work and Proof-of-Stake consensus protocol to append a block recording the new service reputations onto the MEC blockchain. We propose a reinforcement learning (RL) based edge central processing unit (CPU) allocation algorithm without knowing the mobile service generation model and the network model in the dynamic edge computing process and a deep RL version to further improve the computational performance. The security performance is analyzed and the performance bound of the edge utility is provided. Experimental results show that this framework suppresses the selfish edge attacks, decreases the response latency and saves the energy compared with a benchmark MEC scheme.
U. Sailesh, T. Tharun, U. Aditya Varma, Shilpa Shilpa · 5 authors
File authorization is a process of either giving or denying access to a system which in order gives the permission for client to access information depending on the client's profile. Most of the security frameworks depend on a advance procedure which consists of two steps. Confirmation: The first stage which guarantees about the user personality. Approval is subsequent step, which grants the user to get to the access dependent on the user's character. Act in the present day frameworks rely upon trusted third party member. We propose using blockchain-based shrewd agreements to encourage secure examination and the board of files without the need of confided in outsider part. Utilizing the Ethereum blockchain, we can store a hash of a private report (an agreement, for instance) alongside an Ethereum Address. This demonstrates in an open and secure manner that the proprietor of the Ethereum Address has marked the archive. Different gatherings to the agreement can sign it too. All they need is a connect to the marking page, which is produced when a client transfers a document.
With the rise of blockchain technology, data sharing between organizations is often established in a distributed blockchain ledger that is decentralized and tamperproof and has a low trust cost. However, traditional blockchain technology does not adequately support the on-chain storage of massive data, and all the on-chain stored data are completely open and transparent to participants; consequently, it is impossible to meet the user's needs for privacy protection. In this study, we designed a scalable data access control method for blockchains; this method extends the storage forms of the blockchain to support the chaining and sharing of large files, ensures that the traceable data in the chaining process cannot be tampered with, and introduces the attribute authority mechanism in peer nodes to improve the reliability and efficiency of attribute authorization. To verify the correctness and security of the method, we built a complete prototype system based on the Hyperledger Fabric license chain and performed rigorous evaluations on the indicators of physical resource consumption and performance using the Hyperledger Caliper evaluation model. The results showed that the proposed method achieved a good balance in terms of performance, safety, and resource consumption indicators.
Yi Liu, Jialiang Peng, Jiawen Kang, Abdullah M. Iliyasu · 6 authors
Federated learning (FL) has recently been proposed as an emerging paradigm to build machine learning models using distributed training datasets that are locally stored and maintained on different devices in 5G networks while providing privacy preservation for participants. In FL, the central aggregator accumulates local updates uploaded by participants to update a global model. However, there are two critical security threats: poisoning and membership inference attacks. These attacks may be carried out by malicious or unreliable participants, resulting in the construction failure of global models or privacy leakage of FL models. Therefore, it is crucial for FL to develop security means of defense. In this article, we propose a blockchain-based secure FL framework to create smart contracts and prevent malicious or unreliable participants from being involved in FL. In doing so, the central aggregator recognizes malicious and unreliable participants by automatically executing smart contracts to defend against poisoning attacks. Further, we use local differential privacy techniques to prevent membership inference attacks. Numerical results suggest that the proposed framework can effectively deter poisoning and membership inference attacks, thereby improving the security of FL in 5G networks.
Internet of Things (IoT) is a set of technologies that enable network-connected devices to perform an action or share data among several connected devices or to a shared database. The actions can be anything from switching on an Air Conditioning device remotely to turning on the ignition of a car through a command issued from a remote location or asking Alexa or Google Assistant to search for weather conditions in an area. IoT has proved to be game-changing for many industries such as Supply Chain, Shipping and Transportation providing updates on the status of shipments in real time. This has resulted in a huge amount of data created by a lot of these devices all of which need to be processed in real time. In this thesis, we propose a method to collect sensor data from IoT devices and use blockchain to store and retrieve the collected data in a secure and decentralized fashion within a closed system, suitable for a single enterprise or a group of companies in industries like shipping where sharing data with each other is required. Much like blockchain, we envision a future where IoT devices can connect and disconnect to distributed systems without causing downtime for the data collection or storage or relying on a cloud-based storage system for synchronizing data between devices. We also look at how the performance of some of these distributed systems like Inter Planetary File System (IPFS) and Ethereum Swarm compare on low-powered devices like the raspberry pi.
Over the years, the flourish of crowd computing has enabled enterprises to accomplish computing tasks through crowdsourcing in a large-scale and high-quality manner, and therefore how to efficiently and securely implement crowd computing becomes a hotspot. Some recent work innovatively adopted a P2P (peer-to-peer) network as the communication environment of crowdsourcing. Based on its decentralized control, issues like single-point-of-failure or DDoS attack can be overcome to some extent, but the huge computing capacity and storage costs required by this scheme is always unbearable. Federated learning is a distributed machine learning that supports local storage of data, and clients implement training through interactive gradient values. In our work, we combine blockchain with federated learning and propose a crowdsourcing framework named CrowdSFL, that users can implement crowdsourcing with less overhead and higher security. In addition, to protect the privacy of participants, we design a new re-encryption algorithm based on Elgamal to ensure that interactive values and other information will not be exposed to other participants outside the workflow. Finally, we have proved through experiments that our framework is superior to some similar work in accuracy, efficiency, and overhead.
The Internet of Things (IoT) changed our lives with huge amounts of data production. Due to source-limited IoT devices, one of the best ways to process the data is cloud storage. However, a series of security and privacy issues arise, such as illegal data access, data tampering, and privacy leak. Though symmetric encryption can guarantee data confidentiality, it cannot realize fine-grained data sharing and searching. The keyword-based searchable attribute-based encryption (KSABE) can achieve data confidentiality and fine-grained access control. More importantly, it realizes a keyword-based search for data users. However, the heavy decryption computation burden and the management of massive user keys appear when implementing attribute-based encryption schemes to IoT. Therefore, this article proposes a blockchain-aided searchable attribute-based encryption (BC-SABE) with efficient revocation and decryption, where the traditional centralized server is replaced with a decentralized blockchain system being in charge of the threshold parameter generation, key management, and user revocation. All revocation tasks are done by the blockchain and it is on longer necessary for ciphertext reencryption and key update. Moreover, users utilize the coalition blockchain to generate partial tokens. Besides, the cloud server contained in our scheme not only stores the massive encrypted data but also performs search and predecryption for users who only require one exponentiation in the group G to decrypt fully. Security analyses prove that our scheme realizes the security under the chosen plaintext attack and the chosen keyword attack. Simulations show that the decryption and token generation cost of our scheme are preferable.
Alexandros Bampoulidis, A. Bruni, Lukas Helminger, Daniel Kales · 6 authors
Recent work has shown that cell phone mobility data has the unique potential to create accurate models for human mobility and consequently the spread of infected diseases [74]. While prior studies have exclusively relied on a mobile network operator’s subscribers’ aggregated data in modelling disease dynamics, it may be preferable to contemplate aggregated mobility data of infected individuals only. Clearly, naively linking mobile phone data with health records would violate privacy by either allowing to track mobility patterns of infected individuals, leak information on who is infected, or both. This work aims to develop a solution that reports the aggregated mobile phone location data of infected individuals while still maintaining compliance with privacy expectations. To achieve privacy, we use homomorphic encryption, validation techniques derived from zero-knowledge proofs, and differential privacy. Our protocol’s open-source implementation can process eight million subscribers in 70 minutes.
Purpose The purpose of this work is to bridge FL and blockchain technology through designing a blockchain-based smart agent system architecture and applying in FL. and blockchain technology through designing a blockchain-based smart agent system architecture and applying in FL. FL is an emerging collaborative machine learning technique that trains a model across multiple devices or servers holding private data samples without exchanging their data. The locally trained results are aggregated by a centralized server in a privacy-preserving way. However, there is an assumption where the centralized server is trustworthy, which is impractical. Fortunately, blockchain technology has opened a new era of data exchange among trustless strangers because of its decentralized architecture and cryptography-supported techniques. Design/methodology/approach In this study, the author proposes a novel design of a smart agent inspired by the smart contract concept. Specifically, based on the proposed smart agent, a fully decentralized, privacy-preserving and fair deep learning blockchain-FL framework is designed, where the agent network is consistent with the blockchain network and each smart agent is a participant in the FL task. During the whole training process, both the data and the model are not at the risk of leakage. Findings A demonstration of the proposed architecture is designed to train a neural network. Finally, the implementation of the proposed architecture is conducted in the Ethereum development, showing the effectiveness and applicability of the design. Originality/value The author aims to investigate the feasibility and practicality of linking the three areas together, namely, multi-agent system, FL and blockchain. A blockchain-FL framework, which is based on a smart agent system, has been proposed. The author has made several contributions to the state-of-the-art. First of all, a concrete design of a smart agent model is proposed, inspired by the smart contract concept in blockchain. The smart agent is autonomous and is able to disseminate, verify the information and execute the supported protocols. Based on the proposed smart agent model, a new architecture composed by these agents is formed, which is a blockchain network. Then, a fully decentralized, privacy-preserving and smart agent blockchain-FL framework has been proposed, where a smart agent acts as both a peer in a blockchain network and a participant in a FL task at the same time. Finally, a demonstration to train an artificial neural network is implemented to prove the effectiveness of the proposed framework.
Crowdworking platforms provide the opportunity for diverse workers to execute tasks for different requesters. The popularity of the ”gig” economy has given rise to independent platforms that provide competing and complementary services. Workers as well as requesters with specific tasks may need to work for or avail from the services of multiple platforms resulting in the rise of multi-platform crowdworking systems. Recently, there has been increasing interest by governmental, legal and social institutions to enforce regulations, such as minimal and maximal work hours, on crowdworking platforms. Platforms within multi-platform crowdworking systems, therefore, need to collaborate to enforce cross-platform regulations. While collaborating to enforce global regulations requires the transparent sharing of information about tasks and their participants, the privacy of all participants needs to be preserved. In this paper, we propose an overall vision exploring the regulation, privacy, and architecture dimensions for the future of work multi-platform crowdworking environments. We then present Separ, a multi-platform crowdworking system that enforces a large sub-space of practical global regulations on a set of distributed independent platforms in a privacy-preserving manner. Separ, enforces privacy using lightweight and anonymous tokens, while transparency is achieved using fault-tolerant blockchain ledgers shared among multiple platforms. The privacy guarantees of Separ against covert adversaries are formalized and thoroughly demonstrated, while the experiments reveal the efficiency of Separ in terms of performance and scalability.
May 1, 2020·2020 IEEE 6th Intl Conference on Big Data Security on Cloud (BigDataSecurity), IEEE Intl Conference on High Performance and Smart Computing, (HPSC) and IEEE Intl Conference on Intelligent Data and Security (IDS)
Lately there has been an increase in the number of Machine Learning (ML) and Artificial Intelligence (AI) applications ranging from recommendation systems to face to speech recognition. At the helm of the advent of deep learning is the proliferation of data from diverse data sources ranging from Internet-of-Things (IoT) devices to self-driving automobiles. Tapping into this unlimited reservoir of information presents the problem of finding quality data out of a myriad of irrelevant ones, which to this day, has been a significant issue in data science with a direct ramification of this being the inability to generate quality ML models for useful predictive analysis. Edge computing has been deemed a solution to some of issues such as privacy, security, data silos and latency, as it ventures to bring cloud computing services closer to end-nodes. A new form of edge computing known as edge-AI attempts to bring ML, AI, and predictive analytics services closer to the data source (end devices). In this paper, we investigate an approach to bring edge-AI to end-nodes through a shared machine learning model powered by the blockchain technology and a federated learning framework called iFLBC edge. Our approach addresses the issue of the scarcity of relevant data by devising a mechanism known as the Proof of Common Interest (PoCI) to sieve out relevant data from irrelevant ones. The relevant data is trained on a model, which is then aggregated along with other models to generate a shared model that is stored on the blockchain. The aggregated model is downloaded by members of the network which they can utilize for the provision of edge intelligence to end-users. This way, AI can be more ubiquitous as members of the iFLBC network can provide intelligence services to end-users.
May 1, 2020·2020 IEEE 6th Intl Conference on Big Data Security on Cloud (BigDataSecurity), IEEE Intl Conference on High Performance and Smart Computing, (HPSC) and IEEE Intl Conference on Intelligent Data and Security (IDS)
Data Protection regulations, like GDPR, mandate security controls to secure Personal Identifiable Information (PII) of the users which they share with service providers. With the volume of shared data reaching exascale proportions, it is challenging to ensure GDPR compliance in real time. We propose a novel approach that integrates GDPR Ontology with Blockchain to facilitate real time automated data compliance. Our framework ensures data operation is allowed only when validated by data privacy policies in compliance with privacy rules in GDPR. When a valid transaction takes place the PII data is automatically stored off-chain in a database. Our system, built using Semantic Web and Ethereum Blockchain, includes an access-control system that enforces data privacy policy when data is shared with third parties.
Darknet websites, the warm beds for money laundry, child pornography, and illicit drug trafficking, are built on hidden services and anonymous communication protocols. Cryptocurrencies, such as Bitcoin, is the major payment method used on Darknet. In this paper, we summarize and introduce the latest development on de-anonymization techniques used to reveal the hidden information that are helpful for crime investigation, which is a key step for the future research work.