In this article, we study efficient and authorized rewriting of transactions already written to a blockchain. Mutable transactions will make a fraction of all blockchain transactions, but will be a necessity to meet the needs of privacy regulations, such as the General Data Protection Regulation (GDPR). The state-of-the-art rewriting approaches have several shortcomings, such as lack of user anonymity, inefficiency, and absence of revocation mechanisms for entities authorized to mutate transactions. To address this challenge we present \(\mathsf{ReTRACe}\) , an efficient framework for blockchain rewrites. \(\mathsf{ReTRACe}\) is designed by composing a revocable chameleon hash scheme with an ephemeral trapdoor, a revocable fast attribute based encryption scheme, and a dynamic group signature scheme. In this article, (i) we discuss \(\mathsf{ReTRACe}\) and its constituent primitives in detail, (ii) present security analyses of the primitives, and (iii) present experimental results to demonstrate the scalability of \(\mathsf{ReTRACe}\) .

Payment channel networks are a promising solution to the scalability challenge of blockchains and are designed for significantly increased transaction throughput compared to the layer one blockchain. Since payment channel networks are essentially decentralized peer-to-peer networks, routing transactions is a fundamental challenge. Payment channel networks have some unique security and privacy requirements that make pathfinding challenging, for instance, network topology is not publicly known, and sender/receiver privacy should be preserved, in addition to providing atomicity guarantees for payments. In this paper, we present an efficient privacy-preserving routing protocol, SPRITE, for payment channel networks that supports concurrent transactions. By finding paths offline and processing transactions online, SPRITE can process transactions in just two rounds, which is more efficient compared to prior work. We evaluate SPRITE's performance using Lightning Network data and prove its security using the Universal Compos-ability framework. In contrast to the current cutting-edge methods that achieve rapid transactions, our approach significantly reduces the message complexity of the system by 3 orders of magnitude while maintaining similar latencies.

Edge Computing is a new computing paradigm where applications operate at the network edge, providing low-latency services with augmented user and data privacy. A desirable goal for edge computing is pervasiveness, that is, enabling any capable and authorized entity at the edge to provide desired edge services--pervasive edge computing (PEC). However, efficient access control of users receiving services and edge servers handling user data, without sacrificing performance is a challenge. Current solutions, based on "always-on" authentication servers in the cloud, negate the latency benefits of services at the edge and also do not preserve user and data privacy. In this paper, we present APECS, an advanced access control framework for PEC, which allows legitimate users to utilize any available edge services without need for communication beyond the network edge. The APECS framework leverages multi-authority attribute-based encryption to create a federated authority, which delegates the authentication and authorization tasks to semi-trusted edge servers, thus eliminating the need for an "always-on" authentication server in the cloud. Additionally, APECS prevents access to encrypted content by unauthorized edge servers. We analyze and prove the security of APECS in the Universal Composability framework and provide experimental results on the GENI testbed to demonstrate the scalability and effectiveness of APECS.

In this paper, we study efficient and authorized rewriting of transactions already written to a blockchain. Mutable transactions will make a fraction of all blockchain transactions, but will be a necessity to meet the needs of privacy regulations, such as the General Data Protection Regulation (GDPR). The state-of-the-art rewriting approaches have several shortcomings, such as being coarse-grained, inability to expunge data, absence of revocation mechanisms, lack of user anonymity, and inefficiency. We present ReTRACe, an efficient framework for transaction-level blockchain rewrites, that is fine-grained and supports revocation. ReTRACe is designed by composing a novel revocable chameleon hash with ephemeral trapdoor scheme, a novel revocable fast attribute based encryption scheme, and a dynamic group signature scheme. We discuss ReTRACe, and its constituent primitives in detail, along with their security analyses, and present experimental results to demonstrate scalability.

Organized surveillance, especially by governments poses a major challenge to individual privacy, due to the resources governments have at their disposal, and the possibility of overreach. Given the impact of invasive monitoring, in most democratic countries, government surveillance is, in theory, monitored and subject to public oversight to guard against violations. In practice, there is a difficult fine balance between safeguarding individual's privacy rights and not diluting the efficacy of national security investigations, as exemplified by reports on government surveillance programs that have caused public controversy, and have been challenged by civil and privacy rights organizations. Surveillance is generally conducted through a mechanism where federal agencies obtain a warrant from a federal or state judge (e.g., the US FISA court, Supreme Court in Canada) to subpoena a company or service-provider (e.g., Google, Microsoft) for their customers' data. The courts provide annual statistics on the requests (accepted, rejected), while the companies provide annual transparency reports for public auditing. However, in practice, the statistical information provided by the courts and companies is at a very high level, generic, is released after-the-fact, and is inadequate for auditing the operations. Often this is attributed to the lack of scalable mechanisms for reporting and transparent auditing. In this paper, we present SAMPL, a novel auditing framework which leverages cryptographic mechanisms, such as zero knowledge proofs, Pedersen commitments, Merkle trees, and public ledgers to create a scalable mechanism for auditing electronic surveillance processes involving multiple actors. SAMPL is the first framework that can identify the actors (e.g., agencies and companies) that violate the purview of the court orders. We experimentally demonstrate the scalability for SAMPL for handling concurrent monitoring processes without undermining their secrecy and auditability.

Distributed credit networks, such as Ripple~\citeripple and Stellar~\citestellar, are becoming popular as an alternative means for financial transactions. % However, the current designs do not preserve user privacy or are not truly decentralized. % In this paper, we explore the creation of a distributed credit network that preserves user and transaction privacy and unlinkability. We propose BlAnC, a novel, fully decentralized blockchain-based credit network where credit transfer between a sender-receiver pair happens on demand. In BlAnC, multiple concurrent transactions can occur seamlessly, and malicious network actors that do not follow the protocols and/or disrupt operations can be identified efficiently. % for potential debarring by the users from future transactions. % We perform security analysis of our proposed protocols in the universal composability framework to demonstrate its strength, and discuss how our network handles operational dynamics. % We also present preliminary experiments and scalability analyses.