Redactable Signatures for Signed CDA Documents

[[abstract]]The Clinical Document Architecture, introduced by Health Level Seven, is a XML-based standard intending to specify the encoding, structure, and semantics of clinical documents for exchange. Since the clinical document is in XML form, its authenticity and integrity could be guaranteed by the use of the XML signature published by W3C. While a clinical document wants to conceal some personal or private information, the document needs to be redacted. It makes the signed signature of the original clinical document not be verified. The redactable signature is thus proposed to enable verification for the redacted document. Only a little research does the implementation of the redactable signature, and there still not exists an appropriate scheme for the clinical document. This paper will investigate the existing web-technologies and find a compact and applicable model to implement a suitable redactable signature for the clinical document viewer.[[notice]]補正完畢[[incitationindex]]SC

A General Framework for Redactable Signatures and New Constructions

A redactable signature scheme (RSS) allows removing parts of a signed message by any party without invalidating the respective signature. State-of-the-art constructions thereby focus on messages represented by one specific data structure, e.g., lists, sets or trees, and adjust the security model accordingly. To overcome the necessity for this myriad of models, we present a general framework covering arbitrary data-structures and even more sophisticated possibilities. For example, we cover fixed elements which must not be redactable and dependencies between elements. Moreover, we introduce the notion of designated redactors, i.e., the signer can give some extra information to selected entities which become redactors. In practice, this often allows to obtain more efficient schemes. We then present two RSSs; one for sets and one for lists, both constructed from any EUF-CMA secure signature scheme and indistinguishable cryptographic accumulators in a black-box way and show how the concept of designated redactors can be used to increase the efficiency of these schemes. Finally, we present a black-box construction of a designated redactor RSS by combining an RSS for sets with non-interactive zero knowledge proof systems. All the three constructions presented in this paper provide transparency, which is an important property, but quite hard to achieve, as we also conceal the length of the original message and the positions of the redactions

Fully Invisible Protean Signatures Schemes

Protean Signatures (PS), recently introduced by Krenn et al. (CANS \u2718), allow a semi-trusted third party, named the sanitizer, to modify a signed message in a controlled way. The sanitizer can edit signer-chosen parts to arbitrary bitstrings, while the sanitizer can also redact admissible parts, which are also chosen by the signer. Thus, PSs generalize both redactable signature (RSS) and sanitizable signature (SSS) into a single notion. However, the current definition of invisibility does not prohibit that an outsider can decide which parts of a message are redactable - only which parts can be edited are hidden. This negatively impacts on the privacy guarantees provided by the state-of-the-art definition. We extend PSs to be fully invisible. This strengthened notion guarantees that an outsider can neither decide which parts of a message can be edited nor which parts can be redacted. To achieve our goal, we introduce the new notions of Invisible RSSs and Invisible Non-Accountable SSSs (SSS\u27), along with a consolidated framework for aggregate signatures. Using those building blocks, our resulting construction is significantly more efficient than the original scheme by Krenn et al., which we demonstrate in a prototypical implementation

SoK: Privacy-Enhancing Technologies in Finance

Recent years have seen the emergence of practical advanced cryptographic tools that not only protect data privacy and authenticity, but also allow for jointly processing data from different institutions without sacrificing privacy. The ability to do so has enabled implementations a number of traditional and decentralized financial applications that would have required sacrificing privacy or trusting a third party. The main catalyst of this revolution was the advent of decentralized cryptocurrencies that use public ledgers to register financial transactions, which must be verifiable by any third party, while keeping sensitive data private. Zero Knowledge (ZK) proofs rose to prominence as a solution to this challenge, allowing for the owner of sensitive data (e.g. the identities of users involved in an operation) to convince a third party verifier that a certain operation has been correctly executed without revealing said data. It quickly became clear that performing arbitrary computation on private data from multiple sources by means of secure Multiparty Computation (MPC) and related techniques allows for more powerful financial applications, also in traditional finance. In this SoK, we categorize the main traditional and decentralized financial applications that can benefit from state-of-the-art Privacy-Enhancing Technologies (PETs) and identify design patterns commonly used when applying PETs in the context of these applications. In particular, we consider the following classes of applications: 1. Identity Management, KYC & AML; and 2. Markets & Settlement; 3. Legal; and 4. Digital Asset Custody. We examine how ZK proofs, MPC and related PETs have been used to tackle the main security challenges in each of these applications. Moreover, we provide an assessment of the technological readiness of each PET in the context of different financial applications according to the availability of: theoretical feasibility results, preliminary benchmarks (in scientific papers) or benchmarks achieving real-world performance (in commercially deployed solutions). Finally, we propose future applications of PETs as Fintech solutions to currently unsolved issues. While we systematize financial applications of PETs at large, we focus mainly on those applications that require privacy preserving computation on data from multiple parties