Several cryptographic primitives, especially succinct proofs of various forms, transform the satisfaction of high-level properties to the existence of a polynomial quotient between a polynomial that interpolates a set of values with a cleverly arranged divisor. Some examples are SNARKs, like Groth16, and polynomial commitments, such as KZG. Such a polynomial division naively takes O (n log n) time with Fast Fourier Transforms, and is usually the asymptotic bottleneck for these computations.

The vision of Web3 is to improve user control over data and assets, but one challenge that complicates this vision is the prevalence of non-transparent, scam-prone applications and vulnerable smart contracts that put Web3 users at risk.While code audits are one solution to this problem, the lack of smart contracts source code on many blockchain platforms, such as Sui, hinders the ease of auditing.A promising approach to this issue is the use of a decompiler to reverse-engineer smart contract bytecode.However, existing decompilers for Sui produce code that is difficult to understand and cannot be directly recompiled.To address this, we developed the SuiGPT Move AI Decompiler (MAD), a Large Language Model (LLM)-powered web application that decompiles smart contract bytecodes on Sui into logically correct, human-readable, and recompilable source code with prompt engineering.Our evaluation shows that MAD's output successfully passes original unit tests and achieves a 73.33% recompilation success rate on real-world smart contracts.Additionally, newer models tend to deliver improved performance, suggesting that MAD's approach will become increasingly effective as LLMs continue to advance.In a user study involving 12 developers, we found that MAD significantly reduced the auditing workload compared to using traditional decompilers.Participants found MAD's outputs comparable to the original source code, improving accessibility for understanding and auditing non-open-source smart contracts.Through qualitative interviews with these developers and Web3 projects, we further discussed the strengths and concerns of MAD.MAD has practical implications for blockchain smart contract transparency, auditing, and education.It empowers users to easily and independently review and audit non-open-source smart contracts, fostering accountability and decentralization.Moreover, MAD's methodology could potentially extend to other smart contract languages, like Solidity, further enhancing Web3 transparency.

Zero-knowledge range proofs (ZKRPs) allow a prover to convince a verifier that a secret value lies in a given interval. ZKRPs have numerous applications: from anonymous credentials and auctions, to confidential transactions in cryptocurrencies. At the same time, a plethora of ZKRP constructions exist in the literature, each with its own trade-offs. In this work, we systematize the knowledge around ZKRPs. We create a classification of existing constructions based on the underlying building techniques, and we summarize their properties. We provide comparisons between schemes both in terms of properties as well as efficiency levels, and construct a guideline to assist in the selection of an appropriate ZKRP for different application requirements. Finally, we discuss a number of interesting open research problems.

status: Published

Base64 encoding has been a popular method to encode binary data into printable ASCII characters. It is commonly used in several serialization protocols, web, and logging applications, while it is oftentimes the preferred method for human-readable database fields. However, while convenient and with a better compression rate than hex-encoding, the large number of base64 variants in relted standards and proposed padding-mode optionality have been proven problematic in terms of security and cross-platform compatibility.

This work presents an approach for compressing hash-based signatures using STARKs (Ben-Sasson et. al.'18). We focus on constructing a hash-based t-of-n threshold signature scheme, as well as an aggregate signature scheme. In both constructions, an aggregator collects individual one-time hash-based signatures and outputs a STARK proof attesting that the signatures are valid and meet the required thresholds. This proof then serves the role of the aggregate or threshold signature. We demonstrate the concrete performance of such constructions, having implemented the algebraic intermediate representations (AIR) for them, along with an experimental evaluation over our implementation of the STARK protocol.

Proof of liabilities (PoL) allows a prover to prove his/her liabilities to a group of verifiers. This is a cryptographic primitive once used only for proving financial solvency but is also applicable to domains outside finance, including transparent and private donations, new algorithms for disapproval voting and publicly verifiable official reports such as COVID-19 daily cases. These applications share a common nature in incentives: it's not in the prover's interest to increase his/her total liabilities. We generalize PoL for these applications by attempting for the first time to standardize the goals it should achieve from security, privacy and efficiency perspectives. We also propose DAPOL+, a concrete PoL scheme extending the state-of-the-art DAPOL protocol but providing provable security and privacy, with benchmark results demonstrating its practicality. In addition, we explore techniques to provide additional features that might be desired in different applications of PoL and measure the asymptotic probability of failure.

This paper presents HashWires, a hash-based range proof protocol that is applicable in settings for which there is a trusted third party (typically a credential issuer) that can generate commitments. We refer to these as “credential-based” range proofs (CBRPs). HashWires improves upon hashchain solutions that are typically restricted to micro-payments for small interval ranges, achieving an exponential speedup in proof generation and verification time. Under reasonable assumptions and performance considerations, a Hash-Wires proof can be as small as 305 bytes for 64-bit integers. Although CBRPs are not zero-knowledge and are inherently less flexible than general zero-knowledge range proofs, we provide a number of applications in which a credential issuer can leverage HashWires to provide range proofs for private values, without having to rely on heavyweight cryptographic tools and assumptions.

As online transactions become increasingly practical, a broad range of industrial and e-government applications have emerged which depend on time-based protection of confidential data. Despite theoretical progress in timed-release cryptography (TRC), there is still no implementation infrastructure that takes advantage of the latest TRC algorithms. The purpose of this paper is to propose such an infrastructure for pairing-based timed-release cryptography (PB-TRC) systems. Our model uses key generation centers (KGCs) which publish decryption keys periodically, and satisfies the security requirements of modern third party-based TRC schemes. Our approach combines the best features of existing models into a generic and complete infrastructure which is to support TRC. It is also "lighter" in terms of complexity and communication, and is as effective (in terms of security and related properties) as the TRC protocol it is used with.

We propose a new server-based efficient protocol for time-release encryption (TRE), or - as sometimes referred to - sending information "into the future". As with other recently-proposed schemes, ours is based on the use of bilinear pairings on any Gap Diffie-Hellman group, allowing absolute release time of the encrypted data. Our protocol possesses the required properties regarding user anonymity and server passivity. It also provides almost-costless scalability in settings with multiple time-servers, and improves significantly upon existing TRE schemes, in terms of computational and communication cost. This makes our approach well-suited to a number of emerging e-applications that require future decryption of confidential data.