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 \(\mathsf{ReTRACe}\) .

The Bitcoin blockchain scalability problem has inspired several off-chain solutions for enabling cryptocurrency transactions, of which Layer-2 systems such as payment channel networks (PCNs) have emerged as a frontrunner. PCNs allow for path-based transactions between users without the need to access the blockchain. These path-based transactions are possible only if a suitable path exists from the sender of a payment to the receiver. In this paper, we propose Auroch, a distributed auction-based pathfinding and routing protocol that takes into account the routing fees charged by nodes along a path. Unlike other routing protocols proposed for PCNs, Auroch takes routing fees into consideration. Auroch maximizes the profit that can be achieved by an intermediate node at the same time minimizing the overall payment cost for the sender.

The Bitcoin scalability problem has led to the development of off-chain financial mechanisms such as payment channel networks (PCNs) which help users process transactions of varying amounts, including micro-payment transactions, without writing each transaction to the blockchain. Since PCNs only allow path-based transactions, effective, secure routing protocols that find a path between a sender and receiver are fundamental to PCN operations. In this paper, we propose RACED, a routing protocol that leverages the idea of Distributed Hash Tables (DHTs) to route transactions in PCNs in a fast and secure way. Our experiments on real-world transaction datasets show that RACED gives an average transaction success ratio of 98.74%, an average pathfinding time of 31.242 seconds, which is 1.65 × 103, 1.8 × 103, and 4 × 102 times faster than three other recent routing protocols that offer comparable security/privacy properties. We rigorously analyze and prove the security of RACED in the Universal Composability framework.

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.

Pervasive Edge Computing (PEC), a recent addition to the edge computing paradigm, leverages the computing resources of end-user devices to execute computation tasks in close proximity to users. One of the primary challenges in the PEC environment is determining the appropriate servers for offloading computation tasks based on factors, such as computation latency, response quality, device reliability, and cost of service. Computation outsourcing in the PEC ecosystem requires additional security and privacy considerations. Finally, mechanisms need to be in place to guarantee fair payment for the executed service(s).

Money laundering using cryptocurrencies has become increasingly prevalent, and global and national regulatory authorities have announced plans to implement stringent anti-money laundering regulations. In this paper, we examine current anti-money laundering (AML) mechanisms in cryptocurrencies and payment networks from a technical and policy perspective, and point out practical challenges in implementing and enforcing them. We first discuss blacklisting, a recently proposed technique to combat money laundering, which seems appealing, but leaves several unanswered questions and challenges with regard to its enforcement. We then discuss payment networks and find that there are unique problems in the payment network domain that might require custom-designed AML solutions, as opposed to general cryptocurrency AML techniques. Finally, we examine the regulatory guidelines and recommendations as laid out by the global Financial Action Task Force (FATF), and the U.S. based Financial Crimes Enforcement Network (FinCEN), and find that there are several ambiguities in their interpretation and implementation. To quantify the effects of money laundering, we conduct experiments on real-world transaction datasets. Our goal in this paper is to survey the landscape of existing AML mechanisms, and focus the attention of the research community on this issue. Our findings indicate the community must endeavor to treat AML regulations and technical methods as an integral part of the systems they build and must strive to design solutions from the ground up that respect AML regulatory frameworks. We hope that this paper will serve as a point of reference for researchers that wish to build systems with AML mechanisms, and will help them understand the challenges that lie ahead.

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.

The following topics are dealt with: data privacy; learning (artificial intelligence); security of data; authorisation; Internet of Things; cryptographic protocols; electronic money; invasive software; mobile computing.

In this paper, we propose a technique for rebalancing link weights in acyclic payment networks, which are peer-to-peer networks with payment channels or links between users. Payment networks such as Ripple and Stellar offer a range of services such as real-time payment transfers, including cross-currency transactions, and are growing in popularity. They incentivize users to use them by offering much lower transaction fees, and quick transfer times as compared to traditional bank wire transfers. The link weights are positive integers, and once two users connected by a link spend their link weight, the link gets exhausted, and no more transactions can be done over the link. In order to allow users to do any more transactions, the link weight must be increased, a process known as rebalancing link weights. Existing methods of rebalancing require users to perform expensive blockchain transactions of closing and refunding the channel; we consider the problem of rebalancing a payment channel in real time in an efficient way. Our decentralized technique of rebalancing will help users in acyclic payment channel networks to rebalance their link weights on an as-needed basis, and with minimal computational cost, and only off-chain transactions.

In this paper, we extend an automated proof-generation tool, AutoG& P with new axioms and formalizations to support composite data types and q-type assumptions, which in turn can be used to automate pairing-based signature schemes. AutoG& P due to Barthe et at. was designed as a tool to automate proofs of cryptographic primitives based on bilinear pairings in the standard model, but the initial version only supported a limited set of data types, limited pairing-based assumptions, and only provided automated proofs for encryption schemes, notably the Boneh-Boyen identity-based encryption scheme. As examples of our extensions, we provide automated proofs for the Boneh-Boyen pairing-based signature schemes under the well-known and widely-used notion of signature security: existential unforgeability under chosen message attacks in the standard model, and the Boneh-Boyen-Shacham group signature scheme, under standard notions of group signature security: anonymity and traceability.

In storage outsourcing services, clients store their data on a potentially untrusted server, which has more computational power and storage capacity than the individual clients. In this model, security properties such as integrity, authenticity, and freshness of stored data ought to be provided, while minimizing computational costs at the client, and communication costs between the client and the server. Using trusted computing technology on the server's side, we propose practical constructions in the provable data possession model that provide integrity and freshness in a dynamic, multi-user setting, where groups of users can update their shared files on the remote, untrusted server. Unlike previous solutions based on a single-user, single-device model, we consider a multi-user, multi-device model. Using trusted hardware on the server helps us to eliminate some of the previously known challenges with this model, such as forking and rollback attacks by the server. We logically separate bulk storage and data authentication issues to different untrusted remote services, which can be implemented either on the same or different physical servers. With only minor modifications to existing services, the bulk storage component can be provided by large-scale storage providers such as Google, CloudDrive, DropBox, and a smaller specialized server equipped with a trusted hardware chip can be used for providing data authentication. Our constructions eliminate client-side storage costs (clients do not need to maintain persistent state), and are suitable for situations in which multiple clients work collaboratively on remotely stored, outsourced data.

In this paper, we show how Trusted Platform Modules (TPMs), standard security hardware devices, can be used with minor modification to efficiently support Secure Function Evaluation (SFE), a fundamental and extremely powerful cryptographic operation. Prior research by others has shown how SFE can benefit from using security hardware, but prior work has used either custom hardware tokens or powerful secure co-processors which require significant changes to current computing systems. In this paper we show that similar techniques can be supported by TPMs with enhancements that are at the level of a firmware upgrade (albeit a secure firmware upgrade endorsed by the TPM manufacturer) - specifically, no new physical devices would need to be purchased or added to most modern business-class systems. This paper describes the specific changes that need to be made, and evaluates efficiency for a simple example in location-based privacy. Our evaluation shows that performance is reasonable for supporting simple privacy-enhanced applications.

Location-based services (LBS) are available on a variety of mobile platforms like cellphones, PDA''s, etc. and an increasing number of users subscribe to and use these services. One of the basic privacy issues with LBS is that a user may not necessarily want to disclose their own location whenever they inquire about the location of places of interest to them e.g., nearest gas station, restaurant etc. The privacy aspect of LBS has received attention recently with a number of privacy-preserving methodologies being proposed for the client-server model where a querying client requests a location-based server to return some location that is of interest to it without revealing its own location to the server. In this paper, we consider privacy issues in the peer-to-peer model of LBS, where a group of users jointly compute a common location of interest to them such as a restaurant where they could all meet. In such scenarios, all peers in the group would like to jointly find a common location but might not want to reveal their individual locations to each other due to trust issues. We model this problem in the secure multi-party computation framework of cryptography and present a solution where all the peers can jointly compute a common location without the need for any user to reveal its individual location to anyone else. To this end, we present two privacy-preserving models and experimentally evaluate the performance of each of them.

An important privacy issue in Location Based Services (LBS) is to hide a user's identity and location while still providing quality location based services. A user's identity can be easily hidden through anonymous web browsing services. However, a user's location can reveal a user's identity. For example, a user at home may want to ask queries such as “Find the nearest hospital around me” through a GPS enabled mobile phone but he may not be willing to dislose his own location. A common way to achieve location privacy is through cloaking, e.g. the client sends a cloaked region to the server and filters the results to find the exact answer. Recently, Private Information Retrieval has been adopted to answer private location-based queries. However, we argue that ensuring the server does not reveal more data than what is queried is important at the same time. In this paper, we propose an efficient two-level solution based on two cryptographic protocols: PIR and Oblivious Transfer. Our solution is a general-purpose one and can use either a two-level PIR [2] or it can use a combination of PIR and Oblivious Transfer [11]. Our approach provides privacy for the user/client, does not use a trusted party or anonymizer, is provably privacy-preserving, and when compared to previous approaches ensures that the server reveals as minimum data as is required, and the data that is released by the server is as fine-grained or precise as possible.