Kevin Joshi, Kaushal Binjola, Rajas Bondale, Avani Sakhapara · 5 authors
The bane of cryptocurrency wallets is that there are too many of them. Many wallets support only a few individual currencies or a set of currencies. They are preferred over generic wallets, which cover all currencies, as each is quite functional and practical for their given currencies over their generic counterparts. We need a system that provides us with all our currencies and their values in one spot so we can have an overview or look at all our investments at a glance. The solution is a Dapp which enables us to link all our wallets in one place and provide a dashboard with the complete crypto net worth across various chains and wallets. An understated privacy issue is seen in many places; like when applying for visas to other countries, we often have to show our bank statements to prove our net worth. This example also holds for cryptocurrencies. If a user wants to reveal all their crypto assets, they have to give an exact distribution of their crypto assets and their wallet addresses to the one asking for the details, impeding their privacy. Once the wallet address is revealed, the asker can continuously keep track of the users’ transaction histories and hence track the user’s current worth at all times, and know about all the transactions the user will do in the future. It is a notable security and privacy issue. The solution to the above problem is a system wherein someone could ask the user whether their net worth is above a particular threshold. The system will return the answer as true or false, but to maintain the integrity of the answer, we will use Zero Knowledge Proof. It will ensure that the total net worth response from all the linked distributed wallets passes the threshold. It will help protect the user’s privacy, and they will no longer have to give an exact distribution of their cryptocurrencies or share their wallet addresses.
Cryptography and Data Security
Financial Distress and Bankruptcy Prediction
Advanced Steganography and Watermarking Techniques
Usually, a payment system contains multiple verification layers, settlement techniques, and intermediary mediums leading to delayed transactions, especially in cross-border scenarios. Concerning the scalability problem, this paper proposes a consortium blockchain-based cross-border payment system ensuring auditability. The proposed system incorporates Zero Knowledge rollups (ZK-rollup), a layer-2 scaling solution, to make it scalable. ZK-rollup aggregate numerous transactions into one proof, trimming on-chain data and thereby enhancing throughput. This integration not only increases transaction speeds but also ensures privacy, as ZK-rollup encrypt transaction features, allowing for audits without compromising confidentiality. With the consortium blockchain, only permissioned financial institutions can participate in the consensus, offering an enhancement of decentralization, efficiency, and auditability. Significant improvement in cross-border payment systems, reduced transaction settlement times, enhanced security measures, etc., imply the efficiency of the proposed scheme.
In the haste of EHR digitization, the protection of patient data becomes paramount, casting a spotlight on small healthcare clinics grappling with the threats of an ever more interconnected healthcare sector. That is due to the existing centralized system and traditional password-based authentications which cause a single point of failure. Besides this patient consent for EHR sharing is narrowed to a one-time consent. To overcome these problems, this study suggests a robust solution that makes data sharing utilizing blockchain while achieving privacy using web-based Zero Knowledge Proofs (ZKP) authentication and Homomorphic Encryption (HE). The research also considers securely outsourcing EHRs to research organizations to improve data exchange for research purposes. Along with patient-controlled encryption, consent control, and time-limited data access, it also explores dynamic consent management while complying with healthcare standards. Lastly, this study stream-lines secure data sharing with insurance providers while ensuring patient privacy and defending against insurance fraud.
Ralph Ankele, Celi, Sofia, Giles, Ralph, Haddadi, Hamed
In the era of data-driven economies, incentive systems and loyalty programs, have become ubiquitous in various sectors, including advertising, retail, travel, and financial services. While these systems offer advantages for both users and companies, they necessitate the transfer and analysis of substantial amounts of sensitive data. Privacy concerns have become increasingly pertinent, necessitating the development of privacy-preserving incentive protocols. Despite the rising demand for secure and decentralised systems, the existing landscape lacks a comprehensive solution. In this work, we propose the BOOMERANG protocol, a novel decentralised privacy-preserving incentive protocol that leverages cryptographic black box accumulators to securely and privately store user interactions within the incentive system. Moreover, the protocol employs zero-knowledge proofs to transparently compute rewards for users, ensuring verifiability while preserving their privacy. To further enhance public verifiability and transparency, we utilise a smart contract on a Layer 1 blockchain to verify these zero-knowledge proofs. The careful combination of black box accumulators and zero-knowledge proofs makes the BOOMERANG protocol highly efficient.
With the continuous development of Internet of vehicles (IoV) services and the continuous influx of network users, how to solve the security and user privacy protection problems of IoV system has become a top priority. In this regard, based on the in-depth analysis and research of the existing cryptographic theory and anonymous authentication scheme, aiming at the key issues in the security architecture of IoV based on blockchain technology, we propose an anonymous authentication scheme for IoV based on private blockchain. We design a private blockchain-based anonymous authentication scheme for IoV. With the help of the non-tampering characteristics of blockchain, through the decentralized and distributed public key infrastructure (PKI), the registration pressure under high load is effectively shared. In addition, based on the system architecture of distributed authentication, we also design the anonymous identity update stage of the vehicle through the form of hybrid chain, which realizes the continuous tracking and tamper resistance of the vehicle anonymous identity binding information. Finally, through the security analysis and the implementation of the simulation system, we prove that the scheme meets the security requirements and effectively improves the efficiency of the anonymous authentication process.
Open access
Blockchain Technology Applications and Security
Advanced Steganography and Watermarking Techniques
The integration of technologies like the Internet of Things (IoT), Big data, and Artificial Intelligence (AI) has empowered modern vehicles with the ability to communicate with one another for better cooperation on the roads. However, the communication between vehicles exposes the whole intelligent transportation system to new attack vectors. Malicious vehicles can spread misleading information, which, if acted upon, might result in traffic congestion, accidents, chaos, and even fatalities. As a countermeasure, the European Telecommunications Standards Institute (ETSI) proposes a framework, TR 103 460, for reporting abnormal behavior. However, there are many shortcomings, such as the absence of a peer-to-peer (P2P) misbehavior reporting (MR) service and the inability to secure the reporter's identity and reported information. To protect vehicles from abuse, we propose a P2P non-interactive zero-knowledge proof-based privacy-preserving MR framework. Upon testing, we found that the proposed framework prevented the disclosure of the reporter's identity and information and reduced the ITS-Stations' (ITS-Ss) exposure to misbehavior by 67.7% and 79.2% in suburban and highway traffic scenarios, respectively.
To improve the security of crowdsourcing, existing studies introduce blockchain to ensure reliability and utilize cryptography (e.g., encryption and zero-knowledge proof) to protect data privacy. Nevertheless, the crowdsourcing process may involve sensitive identity information, and identity protection remains unresolved during stages such as data submission and correct payment. Especially, when workers invoke a smart contract to submit data, it inevitably exposes their identities. Identity disclosure significantly impacts the credibility of crowdsourcing platforms. Existing solutions suggest solving the problem through anonymous token contracts such as Zether. However, tokens can easily result in fund freezing or extra information leakage. Moreover, invoking the contract to submit data will still disclose workers' blockchain accounts. To tackle the identity protection issue, in this paper, we propose AcCrowd which achieves worker anonymity and payment correctness in crowdsourcing systems atop blockchain. We first design a verifiable proxy submission mechanism for data submission, enabling workers to invoke contracts without disclosing their accounts. Then, we introduce and improve the BlockMaze architecture to replace previous anonymous token-based methods, enhancing privacy and flexibility. Besides, we designed a revealed payment mechanism that utilizes an adaptor signature to bind data reveal and reward payment together, simultaneously protecting the requester and worker. Our security and performance evaluations demonstrate the security and practicability of AcCrowd.
With the development of the Internet of Things technology, the smart home industry is rapidly developing, and the collection and use of sensitive user data are also increasing. The issue of user privacy protection is becoming increasingly prominent and has received widespread attention. However, related research is still in its infancy. Firstly, the technical architecture of the smart home system was introduced, and an in-depth analysis was conducted on the privacy issues and the reasons for their increasing severity in the system. Secondly, based on the degree of use of privacy data such as user personal information and behavior, existing smart home systems were classified. Once again, the research status of different technologies in smart home privacy protection, such as access control, policy control, data minimization, network traffic obfuscation, federated learning, homomorphic encryption, and zero knowledge proof, was summarized and organized, and a comparative analysis was conducted on various technologies. Finally, corresponding suggestions were proposed for privacy protection in smart home systems.
Federated learning is an emerging distributed learning paradigm which brings an efficient and privacy-preserving intelligent model for the Internet of Vehicles (IoV). Unfortunately, federated learning is vulnerable to abnormal model attacks as it is hard to authenticate model parameters. Abnormal local models may slow down the convergence rate, reduce the accuracy of global models, and even deliberately control the global model in the attackers' chosen way. Furthermore, an abnormal global model may deduce sensitive information about vehicles and hinder the execution of genuine tasks. Therefore, in this paper, we propose a parameter-authentication federated learning (PAFL) scheme that can protect privacy of vehicles, such as driving habits, and defend against abnormal model attacks simultane-ously. Concretely, we equip the federated learning framework with the zero knowledge proof and Pedersen commitment to prove and authenticate the reliability of model parameters. Security and privacy analysis, as well as performance evaluation show that the PAFL scheme can successfully detect abnormal models with higher detection rate and achieve more secure global aggregation than existing representative schemes.
Lennart Braun, Cyprien Delpech de Saint Guilhem, Robin Jadoul, Emmanuela Orsini · 6 authors
In this work, we extend the MPC-in-the-Head framework, used in recent efficient zero-knowledge protocols, to work over the ring $$\mathbb {Z}_{2^k}$$ , which is the primary operating domain for modern CPUs. The proposed schemes are compatible with any threshold linear secret sharing scheme and draw inspiration from MPC protocols adapted for ring operations. Additionally, we explore various batching methodologies, leveraging Shamir’s secret sharing schemes and Galois ring extensions, and show the applicability of our approach in RAM program verification. Finally, we analyse different options for instantiating the resulting ZK scheme over rings and compare their communication costs.
Michael Eckel, Dominik Roy George, Björn Grohmann, Christoph Krauß
Trusted Platform Modules (TPMs) are used for remote attestation to ensure the authenticity and integrity of software running on a computer system. However, measuring software executed as containers or virtual machines can be challenging as it is measured concurrently, resulting in a jumbled measurement log that is difficult to disentangle. Moreover, disclosing the entire measurement log in traditional binary remote attestation raises privacy and intellectual property concerns. To address these issues, we propose a remote attestation method with constrained disclosure, allowing for selective disclosure of entries in the measurement log using a non-interactive zero-knowledge (NIZK) proof with Schnorr signatures. Our approach is evaluated for security and privacy and proven to be correct, sound, and satisfies the properties of a NIZK proof. Formal verification of our solution with ProVerif also supports our claims. Furthermore, the performance evaluation of our proof-of-concept implementation shows that our contribution is feasible, and the overhead introduced is negligible.
I took a close look at what a blockchain-based compliance layer might actually do for U.S. payment systems using zero-knowledge proofs (ZKPs) as the key ingredient. The study lays out a conceptual model, grounded in current research, and then walks through different architectural options so you can see the trade-offs. The big takeaway? ZKPs appear capable of meeting core U.S. AML and KYC obligations without forcing people to hand over more personal details than necessary. Frankly, that's kind of the point: keep compliance intact, but avoid needless data exposure. The analysis also suggests that a ZKP-enabled layer could smooth regulatory workflows, cut down on operational risk and costs, and perhaps most importantly drastically reduce how much sensitive information gets shared across payment networks. It's promising, if a bit early-stage, but worth paying attention to.
In the realm of contemporary blockchain mechanisms, inherent flaws persist in bandwidth, security, speed, and reliability. Addressing these issues, the research introduces a pioneering approach that meticulously balances and enhances all the solutions to all these issues simultaneously. This solution is designed to accommodate computers of varying bandwidth capacities, fostering inclusivity and accessibility. Moreover, it empowers every computer to contribute resources, fostering a collaborative and community-driven ecosystem. Security, a paramount concern, is fortified through a novel Zero Knowledge Proof method, coupled with randomized key algorithms, ensuring robust protection against potential threats. Simultaneously, the system achieves remarkable speed improvements, promising seamless transactions and data processing. Furthermore, this research opens doors to an era of complete decentralization, where no single entity wields absolute control. Emphasizing transparency and fairness, with a methodology that redefines the landscape of blockchain technology. In addition, the blockchain technology revolutionizes certificate verification, ensuring authenticity and security.
Federated learning (FL) has been widely adopted in various fields of study and business. Traditional centralized FL systems suffer from serious issues. To address these concerns, decentralized federated learning (DFL) systems have been introduced in recent years. With the help of blockchains, they attempt to achieve more integrity and efficiency. However, privacy preservation remains an uncovered aspect of these systems. To tackle this, as well as to scale the blockchain-based computations, we propose a zero-knowledge proof (ZKP)-based aggregator (zkDFL). This allows clients to share their large-scale model parameters with a trusted centralized server without revealing their individual data to other clients. We utilize blockchain technology to manage the aggregation algorithm via smart contracts. The server performs a ZKP algorithm to prove to the clients that the aggregation is done according to the accepted algorithm. Additionally, the server can prove that all inputs from clients have been used. We evaluate our approach using a public dataset related to the wearable Internet of Things. As demonstrated by numerical evaluations, zkDFL introduces verifiability of the correctness of the aggregation process and enhances the privacy protection and scalability of DFL systems, while the gas cost has significantly declined.
With the rising use of aircrafts for operations ranging from disaster-relief to warfare, there is a growing risk of adversarial attacks. Malicious entities often only require the location of the aircraft for these attacks. Current satellite-aircraft communication and tracking protocols put aircrafts at risk if the satellite is compromised, due to computation being done in plaintext. In this work, we present \texttt{SPAM}, a private, secure, and accurate system that allows satellites to efficiently manage and maintain tracking angles for aircraft fleets without learning aircrafts' locations. \texttt{SPAM} is built upon multi-party computation and zero-knowledge proofs to guarantee privacy and high efficiency. While catered towards aircrafts, \texttt{SPAM}'s zero-knowledge fleet management can be easily extended to the IoT, with very little overhead.
We show the following unconditional results on quantum commitments in two related yet different models: 1. We revisit the notion of quantum auxiliary-input commitments introduced by Chailloux, Kerenidis, and Rosgen (Comput. Complex. 2016) where both the committer and receiver take the same quantum state, which is determined by the security parameter, as quantum auxiliary inputs. We show that computationally-hiding and statistically-binding quantum auxiliary-input commitments exist unconditionally, i.e., without relying on any unproven assumption, while Chailloux et al. assumed a complexity-theoretic assumption, ${\bf QIP}\not\subseteq{\bf QMA}$. On the other hand, we observe that achieving both statistical hiding and statistical binding at the same time is impossible even in the quantum auxiliary-input setting. To the best of our knowledge, this is the first example of unconditionally proving computational security of any form of (classical or quantum) commitments for which statistical security is impossible. As intermediate steps toward our construction, we introduce and unconditionally construct post-quantum sparse pseudorandom distributions and quantum auxiliary-input EFI pairs which may be of independent interest. 2. We introduce a new model which we call the common reference quantum state (CRQS) model where both the committer and receiver take the same quantum state that is randomly sampled by an efficient setup algorithm. We unconditionally prove that there exist statistically hiding and statistically binding commitments in the CRQS model, circumventing the impossibility in the plain model. We also discuss their applications to zero-knowledge proofs, oblivious transfers, and multi-party computations.