With the onset of the big data era, designing efficient and effective machine learning algorithms to analyze large-scale data is in dire need. In practice, data is typically generated by multiple parties and stored in a geographically distributed manner, which spurs the study of distributed machine learning. Traditional master-worker type of distributed machine learning algorithms assumes a trusted central server and focuses on the privacy issue in linear learning models, while privacy in nonlinear learning models and security issues are not well studied. To address these issues, in this paper, we explore the blockchain technique to propose a decentralized privacy-preserving and secure machine learning system, called LearningChain, by considering a general (linear or nonlinear) learning model and without a trusted central server. Specifically, we design a decentralized Stochastic Gradient Descent (SGD) algorithm to learn a general predictive model over the blockchain. In decentralized SGD, we develop differential privacy based schemes to protect each party’s data privacy, and propose an l-nearest aggregation algorithm to protect the system from potential Byzantine attacks. We also conduct theoretical analysis on the privacy and security of the proposed LearningChain. Finally, we implement LearningChain on Etheurum and demonstrate its efficiency and effectiveness through extensive experiments.
Edward Yi Chang, Shih-Wei Liao, Chun‐Ting Liu, Wei-Chen Lin · 8 authors
This paper presents requirements to DeepLinQ and its architecture. DeepLinQ proposes a multi-layer blockchain architecture to improve flexibility, accountability, and scalability through on-demand queries, proxy appointment, subgroup signatures, granular access control, and smart contracts in order to support privacy-preserving distributed data sharing. In this data-driven AI era where big data is the prerequisite for training an effective deep learning model, DeepLinQ provides a trusted infrastructure to enable training data collection in a privacy-preserved way. This paper uses healthcare data sharing as an application example to illustrate key properties and design of DeepLinQ.
Lennart Bader, Jens Bürger, Roman Matzutt, Klaus Wehrle
Processes in the insurance economy are often cumbersome and expensive because of the inherently opposing interests of insurers and customers. Smart contracts bear a large potential to simplify these processes and thereby reduce costs. In this paper, we present CAIPY, our smart contract-based ecosystem for simple and transparent car insurance. In CAIPY, smart contracts do not replace but support current processes to enable significant cost savings, e.g., by removing the necessity for manual inspection of insurance claims in presence of tamper-resistant car sensors. However, the involved parties can resort to well-established processes at any time, trading off cost efficiency against process reliability. CAIPY thus showcases how smart contracts can support insurers without introducing new risks.
Data is of unprecedented importance today. The most valuable companies of today treat data as a commodity, which they trade and earn revenues. To facilitate such trading, data marketplaces have emerged. Present data marketplaces are inadequate as they fail to satisfy all the desirable properties - fairness, efficiency, security, privacy and adherence to regulations. In this article, we propose a blockchain enabled data marketplace solution that fulfills all required properties. We outline the design, show how to design such a system and discuss the challenges in building a complete data marketplace.
Yunhui Long, Tanmay Gangwani, Haris Mughees, Carl A. Gunter
Privacy preserving multi-party computation has many applications in areas such as medicine and online advertisements. In this work, we propose a framework for distributed, secure machine learning among untrusted individuals. The framework consists of two parts: a two-step training protocol based on homomorphic addition and a zero knowledge proof for data validity. By combining these two techniques, our framework provides privacy of per-user data, prevents against a malicious user contributing corrupted data to the shared pool, enables each user to self-compute the results of the algorithm without relying on external trusted third parties, and requires no private channels between groups of users. We show how different ML algorithms such as Latent Dirichlet Allocation, Naive Bayes, Decision Trees etc. fit our framework for distributed, secure computing.
Jingwei Liu, Xiaolu Li, Lin Ye, Hongli Zhang · 6 authors
Electronic medical record (EMR) is a crucial form of healthcare data, currently drawing a lot of attention. Sharing health data is considered to be a critical approach to improve the quality of healthcare service and reduce medical costs. However, EMRs are fragmented across decentralized hospitals, which hinders data sharing and puts patients' privacy at risks. To address these issues, we propose a blockchain based privacy-preserving data sharing for EMRs, called BPDS. In BPDS, the original EMRs are stored securely in the cloud and the indexes are reserved in a tamper-proof consortium blockchain. By this means, the risk of the medical data leakage could be greatly reduced, and at the same time, the indexes in blockchain ensure that the EMRs can not be modified arbitrarily. Secure data sharing can be accomplished automatically according to the predefined access permissions of patients through the smart contracts of blockchain. Besides, the joint-design of the CP-ABE-based access control mechanism and the content extraction signature scheme provides strong privacy preservation in data sharing. Security analysis shows that BPDS is a secure and effective way to realize data sharing for EMRs.
Blockchain-enabled Internet of Things (IoT) systems have received extensive attention from academia and industry. Most previous constructions face the risk of leaking sensitive information since the servers can obtain plaintext data from the devices. To address this issue, in this paper, we propose a decentralized outsourcing computation (DOC) scheme, where the servers can perform fully homomorphic computations on encrypted data from the data owner according to the request of the data owner. In this process, the servers cannot obtain any plaintext data, and dishonest servers can be detected by the data owner. Then, we apply the DOC scheme in the IoT scenario to achieve a confidential blockchain-enabled IoT system, called BeeKeeper 2.0. To the best of our knowledge, this is the first work in which servers of a blockchain-enabled IoT system can perform any-degree homomorphic multiplications and any number of additions on encrypted data from devices according to the requests of the devices without obtaining any plaintext data of the devices. Finally, we provide a detailed performance evaluation for the BeeKeeper 2.0 system by deploying it on Hyperledger Fabric and using Hyperledger Caliper for performance testing. According to our tests, the time consumed between the request stage and recover stage is no more than 3.3 s, which theoretically satisfies the production needs.
Extensive online knowledge sharing can be exploited to solve tasks better, faster and cheaper, and it garners considerable interest in institutional cooperation, learning communities, etc., while many technical problems, such as fair exchange in incentive design and security issues are waiting to be solved. Blockchain technique has high accountability and thus has potential to improve the transparency and security of knowledge sharing. In this paper, to address the above problems, first we propose a Reputation Based Knowledge Sharing system in blockchain, called RBKS. The aim of RBKS is to exploit the copyright protection of the knowledge owner using our proposed fine-grained access control system, and to achieve the paid-for content service which allows bystanders who are interested in the shared knowledge to pay a small fee for the access. Second, a reputation evaluation algorithm is introduced as the core of the incentive, and it may possibly be combined with stake in blockchain to form a hybrid stake for the RBKS system. Third, a blockchain and a trusted storage server are employed together for sharing and storing knowledge, and the main procedures are implemented with smart contract in blockchain to ensure secure execution and fairness. Finally, our analysis shows that the RBKS system is feasible and secure.
Since the security and fault tolerance is the two important metrics of the data storage, it brings both opportunities and challenges for distributed data storage and transaction. The traditional transaction system of storage resources, which generally runs in a centralized mode, results in high cost, vendor lock-in, single point failure risk, DDoS attack and information security. Therefore, this paper proposes a distributed transaction method for cloud storage based on smart contract. First, to guarantee the fault tolerance and decrease the storing cost for erasure coding, a VCG-based auction mechanism is proposed for storage transaction, and we deploy and implement the proposed mechanism by designing a corresponding smart contract. Especially, we address the problem - how to implement a VCG-like mechanism in a blockchain environment. Based on private chain of Ethereum, we make the simulations for proposed storage transaction method. The results showed that proposed transaction model can realize competitive trading of storage resources, and ensure the safe and economic operation of resource trading.
In ID-based user authentications, a privacy problem can occur, since the service provider (SP) can accumulate the user's use history from the user ID. As a solution to that problem, group signatures are researched. One of important issues in the group signatures is the user revocation. Previously, an efficient revocable scheme with signing/verification of constant complexity was proposed. In this scheme, users are managed by a binary tree, and a list of revoked user information, called a revocation list (RL), is used for revocation. However, the scheme suffers from the large RL. Recently, an extended scheme has been proposed, where the RL size is reduced by compressing RL. On the other hand, there is a problem that some overhead occurs in the authentication as a price for reducing the size of RL. In this research, we propose an extended scheme where the authentication is sped up by reducing the number of zero-knowledge proofs. Furthermore, we implemented it on a PC and shows the effectiveness. The verification time is about 30% shorter than the previous scheme.
Mu Yang, Andrea Margheri, Runshan Hu, Vladimiro Sassone
Cloud federation is an emergent cloud-computing paradigm that allows services from different cloud systems to be aggregated in a single pool. To support secure data sharing in a cloud federation, anonymization services that obfuscate sensitive datasets under differential privacy have been recently proposed. However, by outsourcing data protection to the cloud, data owners lose control over their data, raising privacy concerns. This is even more compelling in multi-query scenarios in which maintaining privacy amounts to controlling the allocation of the so-called privacy budget. In this paper, we propose a blockchain-based approach that enables data owners to control the anonymization process and that enhances the security of the services. Our approach relies on blockchain to validate the usage of the privacy budget and adaptively change its allocation through smart contracts, depending on the privacy requirements provided by data owners. Prototype implementation with the Hyperledger permissioned blockchain validates our approach with respect to privacy guarantee and practicality.
Presented here is a functional implementation of Distributed Ledger Technology applied to the task of cataloguing CCTV video evidence. We describe and demonstrate a prototype camera that participates in blockchain creation in real time, and the system designed to manage and coordinate its distribution and use. This application is of specific interest to law enforcement agencies charged with the management of high volumes of CCTV evidence. We discuss applicability and scalability with reference to simulation results and real-world testing. The combination of blockchain technology with a novel digital watermarking application is demonstrated here providing immediate benefit against an existing real-world problem of trustworthy evidence protection in distributed network environments.
Open access
Advanced Steganography and Watermarking Techniques
A significant progress in modern power grids is witnessed by the tendency of becoming complex cyber‐physical systems. As a fundamental physical infrastructure, smart meter in the demand side provides real‐time energy consumption information to the utility. However, ensuring information security and privacy in the meter data aggregation process is a non‐trivial task. This study proposes a distributed, privacy‐preserving, and secure meter data aggregation framework, backed up by Blockchain and homomorphic encryption (HE) technologies. Meter data are aggregated and verified by a hierarchical Blockchain system, in which the consensus mechanism is supported by the practical Byzantine fault tolerance algorithm. On the top of the Blockchain system, HE technology is used to protect the privacy of individual meter data items during the aggregation process. Performance analysis is conducted to validate the proposed method.
Matteo Maffei, Giulio Malavolta, Manuel Reinert, Dominique Schröder
Cloud storage has rapidly become a cornerstone of many IT infrastructures, constituting a seamless solution for the backup, synchronization, and sharing of large amounts of data. Putting user data in the direct control of cloud service providers, however, raises security and privacy concerns related to the integrity of outsourced data, the accidental or intentional leakage of sensitive information, the profiling of user activities and so on. Furthermore, even if the cloud provider is trusted, users having access to outsourced files might be malicious and misbehave. These concerns are particularly serious in sensitive applications like personal health records and credit score systems. To tackle this problem, we present [Formula: see text], a definitional framework for Group Oblivious RAM, in which we formalize several security and privacy properties such as secrecy, integrity, anonymity, and obliviousness. [Formula: see text] allows per entry access control, as selected by the data owner. [Formula: see text] is the first framework to define such a wide range of security and privacy properties for outsourced storage. Regarding obliviousness, we tackle two different attacker models: our first definition protects against an honest-but-curious server while our second definition protects against such a server colluding with malicious clients. In the latter model, we prove a server-side computational lower bound of [Formula: see text] where n is the number of entries in the database, i.e., every operations requires to process a constant fraction of the database. Furthermore, we present two constructions: a pure cryptographic instantiation, which achieves an [Formula: see text] amortized communication and computation complexity and a construction based on a trusted proxy with logarithmic communication and server-side computational complexity. The second construction bypasses the previously established lower bound leveraging a trusted party. Both schemes achieve secrecy, integrity, and obliviousness with respect to a server colluding with malicious clients, but not anonymity due to the deployed access control mechanism. In the former model, we present a cryptographic system that achieves secrecy, integrity, obliviousness, and anonymity. In the process of designing an efficient construction, we developed three new, generally applicable cryptographic schemes, namely, batched zero-knowledge proof of shuffle correctness, the hash-and-proof paradigm, which even improves upon the former, and an accountability technique based on chameleon signatures, which we consider of independent interest. We implemented our constructions in Amazon Elastic Compute Cloud (EC2) and ran a performance evaluation demonstrating the scalability and efficiency of our construction.
Attribute-based encryption (ABE) offers a promising solution for flexible access control over sensitive personal health records in a mobile healthcare system on top of a public cloud infrastructure. However, ABE cannot be simply applied to lightweight devices due to its substantial computation cost during decryption. This problem could be alleviated by delegating significant parts of the decryption operations to computationally powerful parties, such as cloud servers, but the correctness of the delegated computation would be at stake. Thus, previous works enabled users to validate the partial decryption by employing a cryptographic commitment or message authentication code (MAC). This paper demonstrates that the previous commitment or MAC-based schemes cannot support verifiability in the presence of potentially malevolent cloud servers. We propose two concrete attacks on previous commitment or MAC-based schemes. We propose an effective countermeasure scheme for securing resource-limited mobile healthcare systems and provide a rigorous security proof in the standard model, demonstrating that the proposed scheme is secure against our attacks. The experimental analysis shows that the proposed scheme provides the similar performance compared with the previous commitment-based schemes and outperforms the MAC-based scheme.
Currently, the number of Internet of Thing (IoT) devices making up the IoT is more than 11 billion and this number has been continuously increasing. The prevalence of these devices leads to an emerging IoT business model called Device-as-a-service(DaaS), which enables sensor devices to collect data disseminated to all interested devices. The devices sharing data with other devices could receive some financial reward such as Bitcoin. However, side-channel attacks, which aim to exploit some information leaked from the IoT devices during data trade execution, are possible since most of the IoT devices are vulnerable to be hacked or compromised. Thus, it is challenging to securely realize data trading in IoT environment due to the information leakage such as leaking the private key for signing a Bitcoin transaction in Bitcoin system. In this paper, we propose LRCoin, a kind of leakage-resilient cryptocurrency based on bitcoin in which the signature algorithm used for authenticating bitcoin transactions is leakage-resilient. LRCoin is suitable for the scenarios where information leakage is inevitable such as IoT applications. Our core contribution is proposing an efficient bilinear-based continual-leakage-resilient ECDSA signature. We prove the proposed signature algorithm is unforgeable against adaptively chosen messages attack in the generic bilinear group model under the continual leakage setting. Both the theoretical analysis and the implementation demonstrate the practicability of the proposed scheme.
Open access
3 source records
Blockchain Technology Applications and Security
Cryptography and Data Security
Advanced Steganography and Watermarking Techniques
This thesis focuses on the issues between a blockchain technology and the new European Union General Data Protection Regulation (GDPR). The Blockchain technology is a rather new technology which potential has been recognised only in the recent years. Essentially, a blockchain is a distributed database in which data is stored in blocks, which form a chronological chain of blocks. Blockchains have many types and possible use cases, but this research focuses on public and permissionless blockchains, which primary objective is to enable individuals to transact with each other without centralised intermediaries. \n \nThe GDPR entered into force on 25 May 2018. The GDPR was not drafted taking account of distributed ledger technologies, such as the blockchain technology, which has raised several points of tension between the regulation and the technology. The primary focus of this thesis is on the conflict between the ‘immutability’ of blockchain technology and the right to erasure under Article 17 of the GDPR. One of the main features of blockchains is the immutability, that is to say, data on old blocks is extremely difficult to modify or delete. This feature seems prima facie to conflict with Article 17 of the GDPR that provides data subjects with the right to request erasure of their personal data under certain conditions. \n \nFirstly, this thesis analyses the current state of the conflict. Before analysing the conflict, the research addresses two essential preliminary questions: the question about anonymisation and personal data and the question about allocation of responsibilities on blockchains. After that, different solutions proposed to reconcile the conflict are analysed to understand the current situation. While public and permissionless blockchains currently may infringe Article 17 of the GDPR, there are potential solutions for the conflict in the future. \n \nThe second purpose of this thesis is to identify relevant legal problems and propose how to address the problems in the future. Blockchain developers should consider data protection obligations already in the design phase. From the legal side, this research has provided flexible interpretations for the legal problems that could help to comply with the right to erasure. There is a need for a flexible approach to the problems between the regulation and the technology.
Chengjun Cai, Jian Weng, Xingliang Yuan, Cong Wang
Blockchain has led the trend of decentralized applications and shown great use beyond cryptocurrencies. Decentralized storage such as Storj and Sia leverages blockchain to establish an open platform for sharing economy, which provides private and reliable file-outsourcing services. However, the ubiquitous keyword search function over encrypted files is yet to be supported. To enable this function, we first apply searchable encryption techniques to the decentralized setting. But this primitive can hardly ensure the service integrity. The reason is that decentralized storage commonly faces severe threats from both clients and service peers. Service peers may return partial or incorrect results, while clients may intentionally slander the service peers to avoid payments. To address these threats, we utilize the smart contract to record the logs of encrypted search (aka evidence) on the blockchain, and devise a fair protocol to handle disputes and issue fair payments. Using a dynamic-efficient searchable encryption scheme as an instantiation, we craft a concrete scheme that preserves encrypted search capability and enforces ecosystem healthiness, so that service peers are incentivized to make real efforts and jointly guarantee service reliability. We implement our scheme in Python and Solidity, and test its search performance and transaction costs on Ethereum.