Blockchain interoperability is increasingly recognized as the centerpiece for robust interactions among decentralized services. Blockchain ledgers are generally tamper-proof and thus enforce non-repudiation for transactions recorded within the same network. However, such a guarantee does not hold for cross-blockchain transactions. When disruptions occur due to malicious activities or system failures within one blockchain network, foreign networks can take advantage by denying legitimate claims or mounting fraudulent liabilities against the defenseless network. In response, this article introduces InterSnap, a novel blockchain snapshot archival methodology, for enabling auditability of cross-blockchain transactions, enforcing non-repudiation. InterSnap introduces cross-chain transaction receipts that ensure their irrefutability. Snapshots of ledger data along with these receipts are utilized as non-repudiable proof of bilateral agreements among different networks. InterSnap enhances system resilience through a distributed snapshot generation process, need-based snapshot scheduling process, and archival storage and sharing via decentralized platforms. Through a prototype implementation based on Hyperledger Fabric, we conducted experiments using on-premise machines, AWS public cloud instances, as well as a private cloud infrastructure. We establish that InterSnap can recover from malicious attacks while preserving cross-chain transaction receipts. Additionally, our proposed solution demonstrates adaptability to increasing loads while securely transferring snapshot archives with minimal overhead.

Autonomous vehicles (AVs) are gaining in popularity over the years as a viable cab service apps as well as for personal use. However, incidents of crashes involving AVs continue to occur, adversely affecting their prospects for widespread acceptance by both end users and regulatory authorities. While such cases are routinely investigated, in the absence of a human to testify on what caused the crash, one has to rely solely on available data. It is therefore imperative that the data logged by AVs is accessible to the concerned parties in a trustworthy manner. In this paper, we present AVChain—a novel framework for using a permissioned blockchain like HyperLedger Fabric (HLF) to record and share AV data comprised of sensors, actuators, maps, planning algorithms and machine learning models so that the data stays immutable even in the face of cross blaming among involved parties. Since the data volume is extremely large, we appropriately compress and down sample the same before storing in a distributed file system, namely, IPFS (Inter-Planetary File System). The hashes of such IPFS data called Content Ids (CIDs) are committed to the HLF network for making them tamper proof. The HLF ledger can later be queried to obtain the CIDs, which are then further used to retrieve and un-compress the original data from IPFS. Effectiveness and usability of AVChain is demonstrated by generating AV data from CARLA, which is a widely used open source AV simulator. For sharing AV data across organizations like sensor and actuator suppliers, map service providers, machine learning model developers and law enforcement authorities, the Weaver tool has been used to make multiple HLF networks interoperate. We have also developed a web application to demonstrate the working of AVChain. Results of an extensive set of experiments establish the efficacy of our approach.

In the realm of enterprise blockchain systems, robust access control is crucial for enabling secure exchange of data and assets across networks. In this paper, we introduce InterAcct, designed to address the intricacies of access control for cross-chain interactions between permissioned networks. InterAcct enforces access control at the source within the originating network as well as for ledger data and assets in the destination network after validating cross-consortium requests. We implement InterAcct using Hyperledger Cacti as the data interoperability layer across Hyperledger Fabric ledgers. We establish that InterAcct handles cross-chain access control for request-responses with less than 2 second latency overhead while adapting to increasing workloads.

Interoperable blockchain networks require robust redaction control mechanisms to uphold privacy, regulatory compliance, and trust, especially when managing modifications and deletions across multiple autonomous systems. While existing redaction frameworks primarily apply to isolated blockchains, cross-network redaction needs a separate handling process due to varying governance models across chains and inter-consortium trust. In this paper, we propose ReAcct, a comprehensive redaction control framework for blockchain interoperations that regulates and validates redaction requests across chains. ReAcct refines internal metadata for redaction request and response exchange to protect sensitive information. It also enables proof of redaction across interconnected networks. We present a rigorous system architecture, incorporating a detailed threat and trust model for cross-network redaction control. We implement ReAcct using Hyperledger Cacti as the interoperability layer for redaction request response communication exchange between Hyperledger Fabric networks and leverage IPFS for distributed storage. Our evaluation shows that ReAcct effectively enforces redaction policies across interoperable blockchains, maintaining low performance overhead and demonstrating scalability under increasing workload.

Federated Learning (FL) is a collaborative machine learning approach that enables data owning nodes to retain their data locally, preventing its transfer to a central server. It involves sharing only the local model parameters with the server to update a global model, which is then disseminated back to the local nodes. Despite its iterative convergence, FL has several limitations, such as the risk of single-point failure, inadequate incentives for participating nodes, and potential privacy breaches. While Local Differential Privacy (LDP) is often used to mitigate privacy concerns, the other challenges of FL have not yet been addressed comprehensively, even for Locally Differentially Private Federated Learning (LDP-FL). We propose an integrated approach that uses permissioned blockchains to guard against a single point of failure and a token-based incentivization (TBI) mechanism for encouraging participation in LDP-FL. In our scheme, participating nodes receive tokens upon sharing their model parameters, which can subsequently be used to access updated global models. The number of tokens awarded for parameter sharing is determined by ϵ - the privacy factor of LDP, ensuring that the nodes do not overly obfuscate the data they share. We demonstrate the feasibility of our approach by developing the Blockchain-based TBI-LDP-FL framework (hereinafter, referred to as BTLF) on HyperLedger Fabric. Extensive results of experimentation establish the efficacy of BTLF.

The concept of a decentralized web has been realized with the idea of Web 3.0 through interconnecting over multiple blockchain-based networks. However, blockchain incurs significant time and space overhead. This problem can be solved using snapshots that store the blockchain's states compactly. But, the existing snapshot mechanism used in Hyperledger Fabric is limited to siloed operations on a single blockchain only. In this paper, we contribute towards overcoming this drawback by developing a novel mechanism for peer selection, snapshot archival, and cross-blockchain sharing of the snapshot. We extend the snapshot collection mechanism in Hyperledger Fabric to implement the above idea and test it over two blockchain networks emulating a decentralized web architecture.

A botnet (or, a network of bots) is an army of compromised machines that are often under the control and coordination of one or multiple sources or work in a peer-to-peer mode through a remote secure channel. Botnet infection modeling enables design for secure systems. This work proposes an innovative approach using the concept of graph theory-based closeness centrality seeded with deep learning to detect peer-to-peer botnet infection across nodes. Compromised networks can be quite big as botnets and malware infections utilize the ubiquitous internet to spread rapidly. Using closeness centrality as one of the major inclination factors of interacting nodes, the deep neural model is applied. The performance of the proposed method is evaluated by identifying botnet traffic with a high true positive rate (bot-infected node detection rate) and a low false-positive rate.