Cryptography has proven to be one of the most contentious areas in modern society. For some it protects the rights of individuals to privacy and security, while for others it puts up barriers against the protection of our society. This book aims to develop a deep understanding of cryptography, and provide a way of understanding how privacy, identity provision and integrity can be enhanced with the usage of encryption. The book has many novel features including: • full provision of Web-based material on almost every topic covered • provision of additional on-line material, such as videos, source code, and labs • coverage of emerging areas such as Blockchain, Light-weight Cryptography and Zero-knowledge Proofs (ZKPs) Key areas covered include: • Fundamentals of Encryption • Public Key Encryption • Symmetric Key Encryption • Hashing Methods • Key Exchange Methods • Digital Certificates and Authentication • Tunneling • Crypto Cracking • Light-weight Cryptography • Blockchain • Zero-knowledge Proofs This book provides extensive support through the associated website of: http://asecuritysite.com/encryption
As IoT becomes omnipresent vast amounts of data are generated, which can be used for building innovative applications. However,interoperability issues and security concerns, prevent harvesting the full potentials of these data. In this paper we consider the use case of data generated by smart buildings. Buildings are becoming ever "smarter" by integrating IoT devices that improve comfort through sensing and automation. However, these devices and their data are usually siloed in specific applications or manufacturers, even though they can be valuable for various interested stakeholders who provide different types of "over the top" services, e.g., energy management. Most data sharing techniques follow an "all or nothing" approach, creating significant security and privacy threats, when even partially revealed, privacy-preserving, data subsets can fuel innovative applications. With these in mind we develop a platform that enables controlled, privacy-preserving sharing of data items. Our system innovates in two directions: Firstly, it provides a framework for allowing discovery and selective disclosure of IoT data without violating their integrity. Secondly, it provides a user-friendly, intuitive mechanisms allowing efficient, fine-grained access control over the shared data. Our solution leverages recent advances in the areas of Self-Sovereign Identities, Verifiable Credentials, and Zero-Knowledge Proofs, and it integrates them in a platform that combines the industry-standard authorization framework OAuth 2.0 and the Web of Things specifications.
Foteini Baldimtsi, Panagiotis Chatzigiannis, Steven Gordon, Phi Hung Le · 5 authors
We present gOTzilla, a protocol for interactive zero-knowledge proofs for very large disjunctive statements of the following format: given publicly known circuit C, and set of values Y = {y1 , . . . , yn }, prove knowledge of a witness x such that C(x) = y1 ∨ C(x) = y2 ∨ · · · ∨ C(x) = yn . These type of statements are extremely important for the proof of assets (PoA) problem in cryptocurrencies where a prover wants to prove the knowledge of a secret key sk that associates with the hash of a public key H(pk) posted on the ledger. We note that the size of n in popular cryptocurrencies, such as Bitcoin, is estimated to 80 million. For the construction of gOTzilla, we start by observing that if we restructure the proof statement to an equivalent of proving knowledge of (x, y) such that (C(x) = y) ∧ (y = y1 ∨ · · · ∨ y = yn )), then we can reduce the disjunction of equalities to 1-out-of-N oblivious transfer (OT). Our overall protocol is based on the MPC in the head (MPCitH) paradigm. We additionally provide a concrete, efficient extension of our protocol for the case where C combines algebraic and non-algebraic statements (which is the case in the PoA application). We achieve an asymptotic communication cost of O(log n) plus the proof size of the underlying MPCitH protocol. While related work has similar asymptotic complexity, our approach results in concrete performance improvements. We implement our protocol and provide benchmarks. Concretely, for a set of size 1 million entries, the total run-time of our protocol is 14.89 seconds using 48 threads, with 6.18 MB total communication, which is about 4x faster compared to the state of the art when considering a disjunctive statement with algebraic and non-algebraic elements.
With the rapid development of physical networks, tens of billions of Internet of Things (IoT) devices have been deployed worldwide. Access control is essential in the IoT system, which manages user access to vital IoT data. However, access control for the IoT is mainly based on centralized trusted servers, which face problems such as a single point of failure and data leakage. To tackle these challenges, we propose an access control framework for the IoT by combining blockchain and Intel software guard extension (SGX) technology. A blockchain validates both IoT devices and edge servers added to the network. The access control contract is deployed on the blockchain, which can manage attribute-based access control policies in a fine-grained manner and make access control decisions flexibly. SGX technology is introduced into the edge computing server to realize the confidentiality of data processing. Finally, we implemented the prototype of the framework on Quorum and conducted extensive experiments and theoretical analyses on the performance of the blockchain. The results of the experimental tests and theoretical analyses show that our framework has more advantages in computing costs and on-chain storage costs.
Stavros Salonikias, Marie Khair, Θεόδωρος Μάστορας, Ioannis Mavridis
The COVID-19 pandemic further outlined the importance of global healthcare services provisioning for diagnosing and treating patients who tend to travel and live for large periods away from home and can be anywhere at any given time. Advances in technology enable healthcare practitioners to access critical data regarding a person’s health status to provide better services. Medical data are sensitive in nature, and therefore, a reliable mechanism should ensure that only authorized entities can access data when needed. This paper, through a layered consideration of a Globalized Healthcare Provisioning Ecosystem (GHPE), reveals the interdependencies among its major components and suggests a necessary abstraction to identify requirements for the design of an access control suitable for the ecosystem. These requirements are imposed by the nature of the medical data as well as by the newly introduced potentials of Internet of Medical Things (IoMT) devices. As a result, an attribute-based access control framework is proposed aiming to provide prompt and secure access to medical data globally by utilizing state-of-the-art technologies and standards, including Next-Generation Access Control (NGAC), blockchain and smart contracts. Three types of smart contracts are proposed that enable access control to implement attribute and policy stores where policy classes and attributes are decentralized and immutable. In addition, the usage of blockchain-based distributed identities allows patients to be in control of access to their medical data and also enables healthcare service providers to access medical data promptly and reliably through the proposed access control framework. The qualitative characteristics of the proposed approach toward a decentralized and patient-centric access control in GHPE are demonstrated and discussed based on an application paradigm.
In this article, we explore the tension between abstraction and composability in web3 today, specifically within identity solutions, and argue that the current standard DID v1.0 is sufficiently under specified, allowing for many methods and instantiations, including blockchain based certificates. We view experiments today in web3 identity as additive and complementary, and argue that often cited differences are of degree and more in form, less in substance. By way of illustration, we compare decentralized naming services and blockchain based identity certificates such as soulbound tokens (SBTs) to decentralized identifiers (DIDs) and verifiable credentials (VCs). Both paradigms, to the extent they can be meaningfully differentiated, share similar potential as well as challenges. Specifically, we refer to fears about non consensual verification (scarlet letters) and show DID method iterations are not immune by issuing an innocuous public scarlet letter to a DIDs associated public address for anyone to see. Moreover, we argue that because SBTs are unspecified, one could characterize SBTs as an iteration, or extension, of VCs that additionally aspire to achieve composability with web3 smart contracts for correct execution of code, privacy, coercion resistance, and censorship resistance. We offer research paths for how VCs can also achieve these properties. We do not comment on cost, scalability, transferability, or common knowledge as they have been previously reviewed.
Yuntao Wang, Haixia Peng, Zhou Su, Tom H. Luan · 6 authors
Proof of work (PoW), as the representative consensus protocol for blockchain, consumes enormous amounts of computation and energy to determine bookkeeping rights among miners but does not achieve any practical purposes. To address the drawback of PoW, we propose a novel energy-recycling consensus mechanism named platform-free proof of federated learning (PF-PoFL), which leverages the computing power originally wasted in solving hard but meaningless PoW puzzles to conduct practical federated learning (FL) tasks. Nevertheless, potential security threats and efficiency concerns may occur due to the untrusted environment and miners’ self-interested features. In this paper, by devising a novel block structure, new transaction types, and credit-based incentives, PF-PoFL allows efficient artificial intelligence (AI) task outsourcing, federated mining, model evaluation, and reward distribution in a fully decentralized manner, while resisting spoofing and Sybil attacks. Besides, PF-PoFL equips with a user-level differential privacy mechanism for miners to prevent implicit privacy leakage in training FL models. Furthermore, by considering dynamic miner characteristics (e.g., training samples, non-IID degree, and network delay) under diverse FL tasks, a federation formation game-based mechanism is presented to distributively form the optimized disjoint miner partition structure with Nash-stable convergence. Extensive simulations validate the efficiency and effectiveness of PF-PoFL.
Dexin Zhu, Jun Zheng, Hu Zhou, Jianan Wu · 6 authors
Traditional video conference systems depend largely on computational complexity to ensure system security, but with the development of high-performance computers, the existing encryption system will be seriously threatened. To solve this problem, a hybrid encryption scheme for quantum secure video conferencing combined with blockchain is proposed in this study. In the system solution architecture, first, the quantum key distribution network is embedded in the classic network; then, the “classical + quantum” hybrid encryption scheme is designed according to the secret level required for the video conference content. Besides, the real-time monitoring module of the quantum key distribution network is designed to ensure that users can check the running state of the network at any time. Meeting minutes can be shared by combining with blockchain. In order to quickly query meeting minutes, a cache-efficient query method based on B+ tree is proposed. The experimental results show that compared with the traditional video conference system, the quantum secure video conference system sufficiently integrates the technical advantages of the quantum key distribution to resist the security threats such as channel eavesdropping and high-performance computational attacks while ensuring the stable operation of the classic system, thus providing a video conference system with a higher security level. Meanwhile, the query time cost of blockchain with different lengths is tested, and the query efficiency of the proposed method is 3.15-times higher than the original query efficiency of blockchain.
Open access
Chaos-based Image/Signal Encryption
Cryptography and Data Security
Advanced Steganography and Watermarking Techniques
The dominance of a few big companies in the storage market arising various concerns including single point of failure, privacy violation, and oligopoly. To eliminate the dependency on such a centralized storage architecture, several Decentralized Storage Network (DSN) schemes such as Filecoin, Sia, and Storj have been introduced. DSNs leverage blockchain technology to create a storage platform such that the micro storage providers can also participate in the storage market. To verify the accurate data storage by the storage providers during a storage contract, DSNs apply a Proof of Storage (PoS) scheme to continuously inspect the storage service. However, continuous verification of the storage provider imposes an extra cost to the network and therefore end-users. Moreover, DSN's PoS verification is vulnerable to a service denying attack in which the storage provider submits valid PoS to the network while denying the service to the client. Considering the benefits and existing challenges of DSNs, this paper introduces a novel incentive-compatible DSN scheme. In this scheme, the PoS is conducted only if the client submits a challenge request. We model the storage service as a non-cooperative repeated dynamic game and set the players' payoffs such that the storage provider's dominant strategy is to honestly follow the storage contract. Our proposed mechanism leverages the smart-contract and oracle network to govern the storage agreement between the client and storage provider efficiently. Furthermore, our scheme is independent of a specific blockchain platform but can be plugged into any blockchain platform with smart-contract execution capability. As a proof of concept, we have implemented our scheme using solidity language and chainlink oracle network. The performance analysis demonstrates the applicability of our scheme. The outcome of this paper is a new incentive-compatible mechanism designed carefully for the blockchain-based DSN. The proposed mechanism utilizes different tools including game-theory, smart-contract, oracle network, and Merkle tree to improve the security and performance of storage verification in DSN.
Francesco Buccafurri, Vincenzo De Angelis, Sara Lazzaro
Anonymous service delivery has attracted the interest of research and the industry for many decades. To obtain effective solutions, anonymity should be guaranteed against the service provider itself. However, if the full anonymity of users is implemented, no accountability mechanism can be provided. This represents a problem, especially when referring to scenarios in which a user, protected by anonymity, may perform illegally when leveraging the anonymous service. In this paper, we propose a blockchain-based solution to the trade-off between anonymity and accountability. In particular, our solution relies on three independent parties (one of which is the service provider itself) such that only the collaboration of all three actors allows for the disclosure of the real identity of the user. In all other cases, anonymity is guaranteed. To show the feasibility of the proposal, we developed a prototype with user-friendly interfaces that minimize the client-side operations. Our solution is then also effective from the point of view of usability.
Yinghui Zhang, Xuanni Wei, Jin Cao, Jianting Ning · 6 authors
With the rapid development of edge computing technologies, smart healthcare significantly improves people’s lives by collecting and analyzing health data in real time. However, security and privacy issues impede the wide deployment of smart healthcare systems (SHS). Most of existing solutions still have drawbacks with respect to computation efficiency and users’ privacy. In this paper, a blockchain-enabled attribute-based access control scheme with hidden policies is proposed for SHS. The scheme introduces multiple authorities to avoid single point failure. Especially, the mode of online-offline encryption relieves users’ online computation burden by transferring computation tasks to the idle time of users, and policy hiding protects users’ sensitive information. Furthermore, fair payments are realized based on blockchain and smart contracts to support the outsourcing of decryption between users and mobile edge computing servers. Finally, the proposed scheme is proven secure in the random oracle model, and experimental results show that it is computationally efficient and hence can be used in the edge computing environment.
Payment channel network (PCN) is a layer-two scaling solution that enables fast off-chain transactions but does not involve on-chain transaction settlement. PCNs raise new privacy issues including balance secrecy, relationship anonymity and payment privacy. Moreover, protecting privacy causes low transaction success rates. To address this dilemma, we propose zk-PCN, a privacy-preserving payment channel network using zk-SNARKs. We prevent from exposing true balances by setting up \textit{public balances} instead. Using public balances, zk-PCN can guarantee high transaction success rates and protect PCN privacy with zero-knowledge proofs. Additionally, zk-PCN is compatible with the existing routing algorithms of PCNs. To support such compatibility, we propose zk-IPCN to improve zk-PCN with a novel proof generation (RPG) algorithm. zk-IPCN reduces the overheads of storing channel information and lowers the frequency of generating zero-knowledge proofs. Finally, extensive simulations demonstrate the effectiveness and efficiency of zk-PCN in various settings.
A fundamental issue in blockchain systems is their scalability in terms of data storage, computation, communication, and security. To resolve this issue, a promising research direction is coding theory, which is widely used for distributed storage, recovery from erasures or channel errors and/or to reduce communication cost. To this end, this article provides the first comprehensive survey of approaches that employ coding theory to scale blockchain systems. It shows how the use of coded symbols or shards allow participants to only store a fraction of the total blockchain, protect against malicious nodes or erasures, ensure data availability in order to promote transparency, and scale the security of sharded blockchains. Further, coded symbols help reduce communication cost when disseminating blocks, which help bootstrap new nodes and speed up consensus of blocks. For each category of solutions, we highlight problems and issues that motivated their designs and use of coding. Moreover, we provide a qualitative analysis of their storage, communication, and computation costs.
Differential Privacy (DP) is often presented as a strong privacy-enhancing technology with broad applicability and advocated as a de facto standard for releasing aggregate statistics on sensitive data. However, in many embodiments, DP introduces a new attack surface: a malicious entity entrusted with releasing statistics could manipulate the results and use the randomness of DP as a convenient smokescreen to mask its nefariousness. Since revealing the random noise would obviate the purpose of introducing it, the miscreant may have a perfect alibi. To close this loophole, we introduce the idea of Interactive Proofs For Differential Privacy, which requires the publishing entity to output a zero knowledge proof that convinces an efficient verifier that the output is both DP and reliable. Such a definition might seem unachievable, as a verifier must validate that DP randomness was generated faithfully without learning anything about the randomness itself. We resolve this paradox by carefully mixing private and public randomness to compute verifiable DP counting queries with theoretical guarantees and show that it is also practical for real-world deployment. We also demonstrate that computational assumptions are necessary by showing a separation between information-theoretic DP and computational DP under our definition of verifiability.
Movsowitz Davidow, Danielle, Manevich, Yacov, Toch, Eran
Differential Privacy (DP) is often presented as a strong privacy-enhancing technology with broad applicability and advocated as a de-facto standard for releasing aggregate statistics on sensitive data. However, in many embodiments, DP introduces a new attack surface: a malicious entity entrusted with releasing statistics could manipulate the results and use the randomness of DP as a convenient smokescreen to mask its nefariousness. Since revealing the random noise would obviate the purpose of introducing it, the miscreant may have a perfect alibi. To close this loophole, we introduce the idea of \textit{Verifiable Differential Privacy}, which requires the publishing entity to output a zero-knowledge proof that convinces an efficient verifier that the output is both DP and reliable. Such a definition might seem unachievable, as a verifier must validate that DP randomness was generated faithfully without learning anything about the randomness itself. We resolve this paradox by carefully mixing private and public randomness to compute verifiable DP counting queries with theoretical guarantees and show that it is also practical for real-world deployment. We also demonstrate that computational assumptions are necessary by showing a separation between information-theoretic DP and computational DP under our definition of verifiability.
Miodrag J. Mihaljević, Lianhai Wang, Shujiang Xu, Milan Todorović
This paper proposes an approach for pool mining in public blockchain systems based on the employment of a recently reported consensus protocol with the puzzle based on a symmetric encryption that provides an energy–space trade-off and reduces energy consumption. The proposed architecture employs a pseudo-symmetric allocation of the resources for the blockchain consensus protocol and provides protection against certain malicious actions of the pool members, as well as a miner’s opportunity for selecting the resources required for participation in the consensus protocol. Given that the considered consensus protocol employs two resources, the proposed architecture uses this two-dimensional nature to provide resistance against block withholding and selfish mining attacks, as well as a reduction in energy spending as a trade-off with the employment of certain memory resources. The high resistance of the proposed pool mining approach against the considered attacks appears to be a consequence of the success probability of the pool in comparison with the success probability of malicious miners. Assuming appropriate selection of the puzzle hardness, the probability that malicious miners can solve the puzzle without the support of the pool manager can be arbitrarily small. Implementation of the proposed approach on a modified Ethereum platform and experimental evaluation issues have also been reported. The conceptual novelty of the proposed pool mining approach is the following: Instead of separation of the blockchain consensus protocol and control of pool miners honest work, this paper proposes an approach where honest work of miners and pool managers is provided by a dedicated application of the considered consensus protocol. Advantages of the proposal in comparison with the previously reported ones include the following: (i) high resistance against block withholding and selfish mining attacks without an additional security procedure; (ii) reduction in the energy required, and at the same time preservationthe security of the consensus protocol; (iii) flexibility of the pool miners regarding selection of the resources that should be employed providing a trade-off between required energy and memory resources. The proposed architecture was implemented employing a dedicated modification of the Ethereum platform and the performed experiments confirmed the feasibility and effectiveness of the proposal.
In recent years, decentralized applications such as Distributed Ledger Technologies and blockchain have evolved as suitable applications for secure sharing of information in a decentralized fashion using privacy preserving techniques like zero-knowledge protocols. However, the biggest issue with the traditional zero-knowledge protocols on a blockchain ledger is their slow performance on big data. This paper presents the advance zero-knowledge ledger by replacing their range-proof technique with the most efficient range-proof technique based on the improved inner product based zero-knowledge proofs. Moreover, this technique allows the aggregation of multiple range-proofs into a single range-proof, which makes the current zero-knowledge ledger system more efficient than the existing one.
The safety-critical scenarios of artificial intelligence (AI), such as autonomous driving, Internet of Things, smart healthcare, etc., have raised critical requirements of trustworthy AI to guarantee the privacy and security with reliable decisions. As a nascent branch for trustworthy AI, federated learning (FL) has been regarded as a promising privacy preserving framework for training a global AI model over collaborative devices. However, security challenges still exist in the FL framework, e.g., Byzantine attacks from malicious devices, and model tampering attacks from malicious server, which will degrade or destroy the accuracy of trained global AI model. In this paper, we shall propose a decentralized blockchain based FL (B-FL) architecture by using a secure global aggregation algorithm to resist malicious devices, and deploying practical Byzantine fault tolerance consensus protocol with high effectiveness and low energy consumption among multiple edge servers to prevent model tampering from the malicious server. However, to implement B-FL system at the network edge, multiple rounds of cross-validation in blockchain consensus protocol will induce long training latency. We thus formulate a network optimization problem that jointly considers bandwidth and power allocation for the minimization of long-term average training latency consisting of progressive learning rounds. We further propose to transform the network optimization problem as a Markov decision process and leverage the deep reinforcement learning based algorithm to provide high system performance with low computational complexity. Simulation results demonstrate that B-FL can resist malicious attacks from edge devices and servers, and the training latency of B-FL can be significantly reduced by deep reinforcement learning based algorithm compared with baseline algorithms.
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.
Mobile edge computing (MEC) offers the infrastructure for improving data caching performance structurally by deploying edge servers at the network edge within users' close geographic proximity. Popular data like viral videos can be cached on edge servers to serve users with low latency. Investigating the integrity of these edge data is critical and challenging as edge servers often suffer from unreliability and constrained resources. Meanwhile, EDI (edge data integrity) investigation must be performed by edge servers collaboratively at the edge to avoid excessive backhaul network traffic. There are two main challenges in practice: 1) there is a lack of Byzantine-tolerant collaborative investigation method; and 2) edge servers may be reluctant to collaborate without proper incentives. To tackle these challenges systematically, this paper proposes a novel scheme named EdgeWatch to enable robust and collaborative EDI investigation in a decentralized manner based on blockchain. Under EdgeWatch, edge servers collaborate on EDI investigation following a novel integrity consensus. A blockchain system comprises of three main components is built as the infrastructure to facilitate integrity consensus: 1) an incentive mechanism that motivates edge servers to participate in EDI investigation; 2) a reputation system that elects reliable leaders for block consensus; and 3) a leader randomization technique that protects leaders from targeted attacks. We evaluate it against three representative schemes experimentally. The results demonstrate the high precision, efficiency, and robustness of EdgeWatch.
Proof-of-Work (PoW) is a Sybil control mechanism adopted in blockchain-based cryptocurrencies. It prevents the attempt of malicious actors to manipulate distributed ledgers. Bitcoin has successfully suppressed double-spending by accepting the longest PoW chain. Nevertheless, PoW encountered several major security issues surrounding mining competition. One of them is a Block WithHolding (BWH) attack that can exploit a widespread and cooperative environment called a mining pool. This attack takes advantage of untrustworthy relationships between mining pools and participating agents. Moreover, detecting or responding to attacks is challenging due to the nature of mining pools. In this paper, however, we suggest that BWH attacks also have a comparable trust problem. Because a BWH attacker cannot have complete control over BWH agents, they can betray the belonging mining pool and seek further benefits by trading with victims. We prove that this betrayal is not only valid in all attack parameters but also provides double benefits; finally, it is the best strategy for BWH agents. Furthermore, our study implies that BWH attacks may encounter self-destruction of their own revenue, contrary to their intention.