Crowdsensing, driven by the proliferation of sensor-rich mobile devices, has emerged as a promising data sensing and aggregation paradigm. Despite useful, traditional crowdsensing systems typically rely on a centralized third-party platform for data collection and processing, which leads to concerns like single point of failure and lack of operation transparency. Such centralization hinders the wide adoption of crowdsensing by wary participants. We therefore explore an alternative design space of building crowdsensing systems atop the emerging decentralized blockchain technology. While enjoying the benefits brought by the public blockchain, we endeavor to achieve a consolidated set of desirable security properties with a proper choreography of latest techniques and our customized designs. We allow data providers to safely contribute data to the transparent blockchain with the confidentiality guarantee on individual data and differential privacy on the aggregation result. Meanwhile, we ensure the service correctness of data aggregation and sanitization by delicately employing hardware-assisted transparent enclave. Furthermore, we maintain the robustness of our system against faulty data providers that submit invalid data, with a customized zero-knowledge range proof scheme. The experiment results demonstrate the high efficiency of our designs on both mobile client and SGX-enabled server, as well as reasonable on-chain monetary cost of running our task contract on Ethereum.
ABSTRACT The Ethereum block chain as a decentralized platform is so successful that many applications deployed on it. However, for the inherent transparency properties and the lack of privacy, deploying a financial application on top of it is always a challenge. In this paper, we tackle this challenge and propose an anonymous sealed-bid auction protocol based on time-released encryption atop Consortium Block chain. We adopt a strict digital certificate-based identity mechanism of the consortium block chain to permit legitimate participants, and utilize the blind signature based on elliptic curve technology to allowing anonymous participation. Moreover, a timed release public key encryption algorithm is adopted to encrypt bids and prevent auctioneer from colluding with bidders. This is completely different from the method (zero-knowledge proof) used in other papers to prevent collusion between auctioneer and bidder. We provide a specific analysis of our protocol, which shows that our protocol meets anonymity and applicability. KEYWORDS Consortium Block chain, Smart Contract, Sealed-Bid Auction, Time-Released Encryption, Blind signature
The core security of proof-of-work (PoW)-based blockchain networks, relies on PoW consensus algorithm and requires miners solving a crypto-puzzles of hash computation. However, the mining process in the blockchain is resource-intensive where only the first miner who completes full PoW will be rewarded. Therefore, miners exhibit malicious behaviors which cause a waste of distributed computation resource, even posing a threat on the efficiency of blockchain networks. In this brief, we propose a new reputation-based mechanism for the PoW computation in the blockchain, in which miners are incentivized to conduct honest mining. Based on the game theory, we design a reputation-based algorithm to encourage honest mining of miners, and thereby increase the overall revenue of the pool. In addition, numerical illustrations are also presented to support the performance of our proposed mechanism.
Machine learning (ML) techniques have been widely used in many smart city sectors, where a huge amount of data is gathered from various (IoT) devices. As a typical ML model, support vector machine (SVM) enables efficient data classification and thereby finds its applications in real-world scenarios, such as disease diagnosis and anomaly detection. Training an SVM classifier usually requires a collection of labeled IoT data from multiple entities, raising great concerns about data privacy. Most of the existing solutions rely on an implicit assumption that the training data can be reliably collected from multiple data providers, which is often not the case in reality. To bridge the gap between ideal assumptions and realistic constraints, in this paper, we propose secureSVM, which is a privacy-preserving SVM training scheme over blockchain-based encrypted IoT data. We utilize the blockchain techniques to build a secure and reliable data sharing platform among multiple data providers, where IoT data is encrypted and then recorded on a distributed ledger. We design secure building blocks, such as secure polynomial multiplication and secure comparison, by employing a homomorphic cryptosystem, Paillier, and construct a secure SVM training algorithm, which requires only two interactions in a single iteration, with no need for a trusted third-party. Rigorous security analysis prove that the proposed scheme ensures the confidentiality of the sensitive data for each data provider as well as the SVM model parameters for data analysts. Extensive experiments demonstrates the efficiency of the proposed scheme.
Donna N. Dillenberger, Petr Novotny, Qurui Zhang, Praveen Jayachandran · 12 authors
Blockchain records track information about financial payments, movements of products through supply chains, identity verification information, and many other assets. Analytics on this data can provide provenance histories, predictive planning, fraud identification, and regulatory compliance. In this paper, we describe analytics engines connected to blockchains to provide easy-to-use configurable dashboards, predictive models, provenance histories, and compliance checking. We also describe how blockchain data can be combined with external data sources for secure and private analytics, enable artificial intelligence (AI) model creation over geographically dispersed data, and create a history of model creation enabling provenance and lineage tracking for trusted AI.
Privacy is supreme in cryptocurrencies since most users do not want to reveal their identities or the transaction amount in financial transactions. Nevertheless, achieving privacy in blockchain-based cryptocurrencies remains challenging since blockchain is by default a public ledger. For instance, Bitcoin provides builtin pseudonymity rather than true anonymity, which can be compromised by analyzing the transactions. Several solutions have been proposed to enhance the transaction privacy of Bitcoin. Unfortunately, full anonymity is not always desirable, because malicious users are able to conduct illegal transactions, such as money laundering and drug trading, under the cover of anonymity in cryptocurrencies. As a result, regulation in blockchain-based cryptocurrencies is very essential. In this article, we analyze the privacy issues in Bitcoin and investigate some existing privacy-enhancing techniques in blockchain- based cryptocurrencies as well as some privacy-focused altcoins. In addition, we review and compare some works dealing with regulation of cryptocurrencies. Finally, we propose two possible solutions from a top view to balance privacy and regulation of blockchain-based cryptocurrencies. One solution is based on decentralized group signature, in which a group manager is responsible for building a group and tracing the real payer of the group in a transaction. The other solution is based on verifiable encryption, in which a tracing manager is not actively involved in normal transactions but can trace suspicious transactions via an encrypted tag.
We put forward the idea that classical blockchains and smart contracts are potentially useful primitives not only for classical cryptography, but for quantum cryptography as well. Abstractly, a smart contract is a functionality that allows parties to deposit funds, and release them upon fulfillment of algorithmically checkable conditions, and can thus be employed as a formal tool to enforce monetary incentives. In this work, we give the first example of the use of smart contracts in a quantum setting. We describe a simple hybrid classical-quantum payment system whose main ingredients are a classical blockchain capable of handling stateful smart contracts, and quantum lightning, a strengthening of public-key quantum money introduced by Zhandry [Eurocrypt 2019]. Our hybrid payment system uses quantum states as banknotes and a classical blockchain to settle disputes and to keep track of the valid serial numbers. It has several desirable properties: it is decentralized, requiring no trust in any single entity; payments are as quick as quantum communication, regardless of the total number of users; when a quantum banknote is damaged or lost, the rightful owner can recover the lost value.
Evangelos Pournaras, Srivatsan Yadhunathan, Ada Diaconescu
Structure plays a key role in learning performance. In centralized computational systems, hyperparameter optimization and regularization techniques such as dropout are computational means to enhance learning performance by adjusting the deep hierarchical structure. However, in decentralized deep learning by the Internet of Things, the structure is an actual network of autonomous interconnected devices such as smart phones that interact via complex network protocols. Self-adaptation of the learning structure is a challenge. Uncertainties such as network latency, node and link failures or even bottlenecks by limited processing capacity and energy availability can significantly downgrade learning performance. Network self-organization and self-management is complex, while it requires additional computational and network resources that hinder the feasibility of decentralized deep learning. In contrast, this paper introduces a self-adaptive learning approach based on holarchic learning structures for exploring, mitigating and boosting learning performance in distributed environments with uncertainties. A large-scale performance analysis with 864,000 experiments fed with synthetic and real-world data from smart grid and smart city pilot projects confirm the cost-effectiveness of holarchic structures for decentralized deep learning.
David Froelicher, Juan Ramón Troncoso-Pastoriza, João Sá Sousa, Jean‐Pierre Hubaux
Data sharing has become of primary importance in many domains such as big-data analytics, economics and medical research, but remains difficult to achieve when the data are sensitive. In fact, sharing personal information requires individuals' unconditional consent or is often simply forbidden for privacy and security reasons. In this paper, we propose Drynx, a decentralized system for privacy-conscious statistical analysis on distributed datasets. Drynx relies on a set of computing nodes to enable the computation of statistics such as standard deviation or extrema, and the training and evaluation of machine-learning models on sensitive and distributed data. To ensure data confidentiality and the privacy of the data providers, Drynx combines interactive protocols, homomorphic encryption, zero-knowledge proofs of correctness, and differential privacy. It enables an efficient and decentralized verification of the input data and of all the system's computations thus provides auditability in a strong adversarial model in which no entity has to be individually trusted. Drynx is highly modular, dynamic and parallelizable. Our evaluation shows that it enables the training of a logistic regression model on a dataset (12 features and 600,000 records) distributed among 12 data providers in less than 2 seconds. The computations are distributed among 6 computing nodes, and Drynx enables the verification of the query execution's correctness in less than 22 seconds.
David Froelicher, Juan Ramón Troncoso-Pastoriza, João M. C. Sousa, Jean‐Pierre Hubaux
Data sharing has become of primary importance in many domains such as\nbig-data analytics, economics and medical research, but remains difficult to\nachieve when the data are sensitive. In fact, sharing personal information\nrequires individuals' unconditional consent or is often simply forbidden for\nprivacy and security reasons. In this paper, we propose Drynx, a decentralized\nsystem for privacy-conscious statistical analysis on distributed datasets.\nDrynx relies on a set of computing nodes to enable the computation of\nstatistics such as standard deviation or extrema, and the training and\nevaluation of machine-learning models on sensitive and distributed data. To\nensure data confidentiality and the privacy of the data providers, Drynx\ncombines interactive protocols, homomorphic encryption, zero-knowledge proofs\nof correctness, and differential privacy. It enables an efficient and\ndecentralized verification of the input data and of all the system's\ncomputations thus provides auditability in a strong adversarial model in which\nno entity has to be individually trusted. Drynx is highly modular, dynamic and\nparallelizable. Our evaluation shows that it enables the training of a logistic\nregression model on a dataset (12 features and 600,000 records) distributed\namong 12 data providers in less than 2 seconds. The computations are\ndistributed among 6 computing nodes, and Drynx enables the verification of the\nquery execution's correctness in less than 22 seconds.\n
The Internet of Things (IoT) is experiencing explosive growth and has gained extensive attention from academia and industry in recent years. Most of the existing IoT infrastructures are centralized, in which the presence of a cloud server is mandatory. However, centralized frameworks suffer from the issues of unscalability and single-point-of-failure. Consequently, decentralized IoT has been proposed by taking advantage of the emerging technology of Blockchain. Voting systems are widely adopted in IoT, such as a leader election in wireless sensor networks. Self-tallying voting systems are alternatives to traditional centralized voting systems in decentralized IoT since the traditional ones are not suitable for such scenarios. Unfortunately, self-tallying voting systems inherently suffer from fairness issues, such as adaptive and abortive issues caused by malicious voters. In this paper, we introduce a framework of self-tallying systems in decentralized IoT based on Blockchain. We propose a concrete construction and prove the proposed system satisfies all the security requirements including fairness, dispute-freeness and maximal ballot secrecy. The implementations on mobile phones demonstrate the practicability of our system.
Cryptocurrencies, led by bitcoin launched in 2009, have obtained wide attention due to the emerging Blockchain in recent years. Anonymous cryptocurrencies are highly essential since users want to preserve their privacy when conducting transactions. However, some users might misbehave with the cover of anonymity such as rampant trafficking and extortion. Thus, it is important to balance anonymity and accountability of anonymous cryptocurrencies. In this paper, we solve this issue by proposing a linkable group signature (LGS) for signing cryptocurrency transactions, which can be used to trace a payer's identity in consortium blockchain based anonymous cryptocurrencies, in case the payer tries illegal activities. A payer keeps anonymous if he/she behaves honestly. We prove that the proposed scheme achieves full-anonymity, full-traceability and linkability in the random oracle. Implementation of the proposed LGS scheme demonstrates its high efficiency thus, can be adopted in anonymous cryptocurrencies in reality.
David Hawig, C. Zhou, Sebastian Fuhrhop, Andre S Fialho · 5 authors
BACKGROUND Distributed ledger technology (DLT) holds great potential to improve health information exchange. However, the immutable and transparent character of this technology may conflict with data privacy regulations and data processing best practices. OBJECTIVE The aim of this paper is to develop a proof-of-concept system for immutable, interoperable, and General Data Protection Regulation (GDPR)–compliant exchange of blood glucose data. METHODS Given that there is no ideal design for a DLT-based patient-provider data exchange solution, we proposed two different variations for our proof-of-concept system. One design was based purely on the public IOTA distributed ledger (a directed acyclic graph-based DLT) and the second used the same public IOTA ledger in combination with a private InterPlanetary File System (IPFS) cluster. Both designs were assessed according to (1) data reversal risk, (2) data linkability risks, (3) processing time, (4) file size compatibility, and (5) overall system complexity. RESULTS The public IOTA design slightly increased the risk of personal data linkability, had an overall low processing time (requiring mean 6.1, SD 1.9 seconds to upload one blood glucose data sample into the DLT), and was relatively simple to implement. The combination of the public IOTA with a private IPFS cluster minimized both reversal and linkability risks, allowed for the exchange of large files (3 months of blood glucose data were uploaded into the DLT in mean 38.1, SD 13.4 seconds), but involved a relatively higher setup complexity. CONCLUSIONS For the specific use case of blood glucose explored in this study, both designs presented a suitable performance in enabling the interoperable exchange of data between patients and providers. Additionally, both systems were designed considering the latest guidelines on personal data processing, thereby maximizing the alignment with recent GDPR requirements. For future works, these results suggest that the conflict between DLT and data privacy regulations can be addressed if careful considerations are made regarding the use case and the design of the data exchange system.
An essential requirement of any information management system is to protect data and resources against breach or improper modifications, while at the same time ensuring data access to legitimate users. Systems handling personal data are mandated to track its flow to comply with data protection regulations. We have built a novel framework that integrates semantically rich data privacy knowledge graph with Hyperledger Fabric blockchain technology, to develop an automated access-control and audit mechanism that enforces users’ data privacy policies while sharing their data with third parties. Our blockchain based data-sharing solution addresses two of the most critical challenges: transaction verification and permissioned data obfuscation. Our solution ensures accountability for data sharing in the cloud by incorporating a secure and efficient system for End-to-End provenance. In this paper, we describe this framework along with the comprehensive semantically rich knowledge graph that we have developed to capture rules embedded in data privacy policy documents. Our framework can be used by organizations to automate compliance of their Cloud datasets.
The advancements in information and communications technology have connected the disconnected, and changed the daily lives of everybody. The automobile industry is comparatively a laggard to connect its disconnected mobility, but new efforts are introduced to put the car on the network as so called connected cars. A single vehicle contains numerous parts to be assembled just as the numerous types of data which can be collected. Like all the connected things being exposed to a cybersecurity concern, the connected cars are also being exposed to cyber-attacks which can exploit your physical safety from privacy. This research proposes security-assured vehicle data platform through closed blockchain for the service provider who facilitates the data for business. The paper is composed of following. First, the paper identifies the type of data which contains the privacy concerns that can be collected from the vehicle. Second, the authors present how the data collected from the vehicle is valuable to multiple parties. Third, the study reviews why the blockchain is an appropriate technology to collect and redistribute the vehicle collected data. Last, the research proposes a vehicle data platform with a blockchain application which assures confidentiality, integrity and accessibility of the data. The expected contributions of this research are following: First, the research identifies and proposes the value of the vehicle collected data. Second, the study determines and tackles the potential exploits from cyber-attacks to the vehicle data platform. Third, the technical extensions from blockchain to the related industries and potential participants of the platform. The study also expects to extend the technical applications with actual vehicle collected data to closed blockchain.
Albert Kofi Kwansah Ansah, Daniel Adu-Gyamfi, Stephen Anokye
Bitcoin ecosystem is supposed to be anonymous and untraceable. Nonetheless, Bitcoin offers weak anonymity in practice. The linkable pseudonymity of Bitcoin system raises privacy concerns for users. There are inputs and outputs of cryptocurrencies that link to Bitcoin public addresses. This is vulnerable to possible linkability and traceability of users’ identity, that can lead to information leakage. Input and output addresses of transactions miss unlinkability in Bitcoin. Several attempts to solve unlinkability and untraceability of users’ transactions found not to satisfy all requirements of a practical anonymity for users to transact business with bitcoins securely and privately. In this paper, the authors focused on preserving identity and transactional behaviour of users in bitcoin cryptocurrency transactions. A secure privacy-preserving scheme is presented. The paper incorporates bilinear pairing, elliptic curve (ECC), ring signature and Zero Knowledge Proof to curb users’ privacy breaches. The authors theoretically analysed, and evaluated proposed scheme, which is proven secure and robust to implement for preserving users’ privacy in bitcoin transactions. The proposal adds a compatible privacy-preserving layer on top of the Bitcoin blockchain, and consistent with the current Bitcoin architecture without offering any modification.
With the increasing popularity of online shopping, privacy concerns in E-commerce are attracting more and more attention. Existing E-commerce models are trapped in a dilemma between the proof of ownership and privacy protection. To address this issue, in this paper we design a privacy-preserving business protocol by employing private smart contracts in the negotiation phase. The protocol allows counterparties make deals without the disclosure of private information such as identities, addresses, and phone numbers. Moreover, we employ the zero-knowledge proof to guarantee the ownership. To understand the feasibility for implementing the proposed model, we also conduct extensive experiments to evaluate the performance of existing blockchain development platforms, Ethereum Quorum and SERO.
Nchinda Nchinda, Agnes Cameron, Kallirroi Retzepi, Andrew Lippman
MedRec is a simple, distributed system for personal control of identity and distribution of personal information. The work is done in the context of a medical information distribution system where patients retain control over who can access their data. We present a new architecture for the MedRec project, creating a network of trusted data repositories, the access to which are determined by a set of ”smart contracts”. These contracts are stored on a distributed ledger maintained by those who generate data. The distributed nature of the system allows unified access from diverse sources in a single application with no intermediary. This increases patient control while retaining a measure of privacy of both data content and source. MedRec is amenable to extensions for decentralized messaging and distribution of information to third parties such as medical researchers, healthcare proxies, and other institutions. The system is based on a blockchain that contains smart contracts defining user identity and distribution specifics.
In vehicular cloud computing, a number of vehicle users act as nodes and communicate with each other to obtain location based services and data. Malicious users or service providers can collect the information of locations travelled and application accessed from vehicle users. Therefore, the authentication of vehicle users becomes challenge with preserving their actual identity and location privacy. In this paper we propose an efficient multi factor authentication scheme for vehicular cloud computing environment. This scheme uses zero knowledge based algorithm for user identity verification. It provides good privacy preservation, so that the adversaries are not able to succeed in tracing any vehicles. The simulation outcomes confirms that the proposed scheme achieves superior compared to existing schemes in terms of computational cost. Therefore, our scheme is appropriate for providing secure solution in vehicular cloud.
Over the last fifty years, medical treatment has seen remarkable advancement, however, the data management and storage systems of medical records has lagged in comparison. In addition, these systems are often inharmonious across platforms and do not put the privacy desires of patients first. While HIPAA and other laws are put in place to protect patient medical record security and privacy, these antiquated systems inherently hinder patient security and privacy. In this paper, we propose a novel data sharing and management scheme that empowers patients over their records by leveraging the security and privacy benefits of blockchain and smart contracts. In comparison to current methods for healthcare records management, our proposed scheme empower patients over their records and minimizes the dependencies on record-generating institutions. It also allows the patients to selectively share their records and disclose certain parts with specific data users based on the privacy preferences desired. In our security and privacy analysis, we show that patients can protect against potential threats to securely and privately share their records. Moreover, in our performance discussions, we show that smart contract design and development is key.
In the Internet of Vehicles (IoV), data sharing among vehicles is critical for improving driving safety and enhancing vehicular services. To ensure security and traceability of data sharing, existing studies utilize efficient delegated proof-of-stake consensus scheme as hard security solutions to establish blockchain-enabled IoV (BIoV). However, as the miners are selected from miner candidates by stake-based voting, defending against voting collusion between the candidates and compromised high-stake vehicles becomes challenging. To address the challenge, in this paper, we propose a two-stage soft security enhancement solution: 1) miner selection and 2) block verification. In the first stage, we design a reputation-based voting scheme to ensure secure miner selection. This scheme evaluates candidates' reputation using both past interactions and recommended opinions from other vehicles. The candidates with high reputation are selected to be active miners and standby miners. In the second stage, to prevent internal collusion among active miners, a newly generated block is further verified and audited by standby miners. To incentivize the participation of the standby miners in block verification, we adopt the contract theory to model the interactions between active miners and standby miners, where block verification security and delay are taken into consideration. Numerical results based on a real-world dataset confirm the security and efficiency of our schemes for data sharing in BIoV.