Error syndromes for heavy hexagonal code and other topological codes such as surface code have typically been decoded by using Minimum Weight Perfect Matching– (MWPM) based methods. Recent advances have shown that topological codes can be efficiently decoded by deploying machine learning (ML) techniques, in particular with neural networks. In this work, we first propose an ML-based decoder for heavy hexagonal code and establish its efficiency in terms of the values of threshold and pseudo-threshold for various noise models. We show that the proposed ML-based decoding method achieves ~ 5 × higher values of threshold than that for MWPM. Next, exploiting the property of subsystem codes, we define gauge equivalence for heavy hexagonal code, by which two distinct errors can belong to the same error class. A linear search-based method is proposed for determining the equivalent error classes. This provides a quadratic reduction in the number of error classes to be considered for both bit flip and phase flip errors and thus a further improvement of ~ 14% in the threshold over the basic ML decoder. Last, a novel technique based on rank to determine the equivalent error classes is presented, which is empirically faster than the one based on linear search.

A Fair Digital Exchange is defined as either all or none of the participants achieving a (predetermined) desirable outcome. This work addresses third party mediated systems for digital content where mutually unknown, and hence non-trusting, buyers, sellers and the mediator (third party) take part in an exchange protocol. We address the lack of guaranteed fairness, as defined above, in the existing platforms for this setting. We present TEDX, a decentralized solution for guaranteed three party fair exchange of digital goods with scalability and support for incremental deployment over the existing (non-fair) platforms. TEDX combines carefully crafted message exchanges with incentive schemes designed to deter malicious behavior. TEDX also leverages ideas from blockchain anchored state-channels to provide trusted execution while minimizing the operational overheads of blockchain. We present the design and a security analysis of TEDX to validate the claimed fairness properties. We also present the details of a prototype implementation of TEDX leveraging Hyperledger Fabric and performance evaluation of the same on a realistic testbed spanning five public cloud zones. Our results indicate that TEDX adds only a minimal overhead of 16% while being 46x faster than a naive blockchain solution, thereby demonstrating that TEDX is scalable.

A core enabler for blockchain or DLT interoperability is the ability to atomically exchange assets held by mutually untrusting owners on different ledgers. This atomic swap problem has been well-studied, with the Hash Time Locked Contract (HTLC) emerging as a canonical solution. HTLC ensures atomicity of exchange, albeit with caveats for node failure and timeliness of claims. But a bigger limitation of HTLC is that it only applies to a model consisting of two adversarial parties having sole ownership of a single asset in each ledger. Realistic extensions of the model in which assets may be jointly owned by multiple parties, all of whose consents are required for exchanges, or where multiple assets must be exchanged for one, are susceptible to collusion attacks and hence cannot be handled by HTLC. In this paper, we generalize the model of asset exchanges across DLT networks and present a taxonomy of use cases, describe the threat model, and propose MPHTLC, an augmented HTLC protocol for atomic multi-owner-and-asset exchanges. We analyze the correctness, safety, and application scope of MPHTLC. As proof-of-concept, we show how MPHTLC primitives can be implemented in networks built on Hyperledger Fabric and Corda, and how MPHTLC can be implemented in the Hyperledger Labs Weaver framework by augmenting its existing HTLC protocol.

Interoperation between permissioned consortium blockchain networks relies on their abilities to discover and validate the identities of each others’ participant organizations. These organizations may possess self-sovereign decentralized identities and verifiable credentials issued by well-known certification authorities. Two mutually untrusting networks of organizations can establish a basis for interoperation if they have one or more certification authorities in common. Yet, for privacy reasons, neither of them may want to expose a priori their entire lists of authorities, necessitating a negotiation process through which common authorities can be identified. In this paper, we analyze this negotiation problem, and propose and analyze two solution approaches, one involving active participation of the trust anchors and the other without involving them.

Decentralized exchange markets leveraging blockchain have been proposed recently to provide open and equal access to traders, improve transparency and avoid single-point-of-compromise of centralized exchanges. However, they compromise on the privacy of traders with respect to their asset ownership, account balance, order details and their identity. In this paper, we present Rialto, a fully decentralized privacy-preserving exchange marketplace with support for matching trade orders, on-chain settlement and market price discovery. Rialto provides order rate and account balance confidentiality and unlinkability between traders and their trade orders, while retaining the desirable properties of a traditional marketplace like front-running resilience and market fairness. We define formal security notions of the marketplace. We perform a detailed evaluation of our solution, demonstrate that it scales well and is suitable for a large class of goods and financial instruments traded in modern exchange markets.

Abstract Traditional zero-knowledge protocols have been studied and optimized for the setting where a single prover holds the complete witness and tries to convince a verifier about a predicate on the witness, without revealing any additional information to the verifier. In this work, we study the notion of distributed-prover zero knowledge (DPZK) for arbitrary predicates where the witness is shared among multiple mutually distrusting provers and they want to convince a verifier that their shares together satisfy the predicate. We make the following contributions to the notion of distributed proof generation: (i) we propose a new MPC-style security definition to capture the adversarial settings possible for different collusion models between the provers and the verifier, (ii) we discuss new efficiency parameters for distributed proof generation such as the number of rounds of interaction and the amount of communication among the provers, and (iii) we propose a compiler that realizes distributed proof generation from the zero-knowledge protocols in the Interactive Oracle Proofs (IOP) paradigm. Our compiler can be used to obtain DPZK from arbitrary IOP protocols, but the concrete efficiency overheads are substantial in general. To this end, we contribute (iv) a new zero-knowledge IOP Graphene which can be compiled into an efficient DPZK protocol. The (D + 1)-DPZK protocol D-Graphene, with D provers and one verifier, admitsO(N1/c) proof size with a communication complexity ofO(D2·(N1−2/c+Ns)), whereNis the number of gates in the arithmetic circuit representing the predicate andNsis the number of wires that depends on inputs from two or more parties. Significantly, only the distributed proof generation in D-Graphene requires interaction among the provers. D-Graphene compares favourably with the DPZK protocols obtained from the state-of-art zero-knowledge protocols, even those not modelled as IOPs.

Abstract Encrypted database systems provide a great method for protecting sensitive data in untrusted infrastructures. These systems are built using either special-purpose cryptographic algorithms that support operations over encrypted data, or by leveraging trusted computing co-processors. Strong cryptographic algorithms (e.g., public-key encryptions, garbled circuits) usually result in high performance overheads, while weaker algorithms (e.g., order-preserving encryption) result in large leakage profiles. On the other hand, some encrypted database systems (e.g., Cipherbase, TrustedDB) leverage non-standard trusted computing devices, and are designed to work around the architectural limitations of the specific devices used. In this work we build StealthDB – an encrypted database system from Intel SGX. Our system can run on any newer generation Intel CPU. StealthDB has a very small trusted computing base, scales to large transactional workloads, requires minor DBMS changes, and provides a relatively strong security guarantees at steady state and during query execution. Our prototype on top of Postgres supports the full TPC-C benchmark with a 30% decrease in the average throughput over an unmodified version of Postgres operating on a 2GB unencrypted dataset.

Functional encryption (FE) is an extremely powerful cryptographic mechanism that lets an authorized entity compute on encrypted data, and learn the results in the clear. However, all current cryptographic instantiations for general FE are too impractical to be implemented. We construct IRON, a provably secure, and practical FE system using Intel's recent Software Guard Extensions (SGX). We show that IRON can be applied to complex functionalities, and even for simple functions, outperforms the best known cryptographic schemes. We argue security by modeling FE in the context of hardware elements, and prove that IRON satisfies the security model.