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 of 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; 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.

Secure distributed generation of RSA moduli (e.g., generating N=pq where none of the parties learns anything about p or q) is an important cryptographic task, that is needed both in threshold implementations of RSA-based cryptosystems and in other, advanced cryptographic protocols that assume that all the parties have access to a trusted RSA modulo. In this paper, we provide a novel protocol for secure distributed RSA key generation based on the Miller-Rabin test. Compared with the more commonly used Boneh-Franklin test (which requires many iterations), the Miller-Rabin test has the advantage of providing negligible error after even a single iteration of the test for large enough moduli (e.g., 4096 bits).

A Single Sign-On (SSO) system allows users to access different remote services while authenticating only once. SSO can greatly improve the usability and security of online activities by dispensing with the need to securely remember or store tens or hundreds of authentication secrets. On the downside, today's SSO providers can track users' online behavior, and collect personal data that service providers want to see asserted before letting a user access their resources. In this work, we propose a new policy-based Single Sign-On service, i.e., a system that produces access tokens that are conditioned on the user's attributes fulfilling a specified policy. Our solution is based on multi-party computation and threshold cryptography, and generates access tokens of standardized format. The central idea is to distribute the role of the SSO provider among several entities, in order to shield user attributes and access patterns from each individual entity. We provide a formal security model and analysis in the Universal Composability framework, against proactive adversaries. Our implementation and benchmarking show the practicality of our system for many real-world use cases.

As governments around the world decide to deploy digital health passports as a tool to curb the spread of Covid-19, it becomes increasingly important to consider how these can be constructed with privacy-by-design.

Single Sign-On (SSO) is becoming an increasingly popular authentication method for users that leverages a trusted Identity Provider (IdP) to bootstrap secure authentication tokens from a single user password. It alleviates some of the worst security issues of passwords, as users no longer need to memorize individual passwords for all service providers, and it removes the burden of these service to properly protect huge password databases. However, SSO also introduces a single point of failure. If compromised, the IdP can impersonate all users and learn their master passwords. To remedy this risk while preserving the advantages of SSO, Agrawal et al. (CCS'18) recently proposed a distributed realization termed PASTA (password-authenticated threshold authentication) which splits the role of the IdP across n servers. While PASTA is a great step forward and guarantees security as long as not all servers are corrupted, it uses a rather inflexible corruption model: servers cannot be corrupted adaptively and - even worse - cannot recover from corruption. The latter is known as proactive security and allows servers to re-share their keys, thereby rendering all previously compromised information useless. In this work, we improve upon the work of PASTA and propose a distributed SSO protocol with proactive and adaptive security (PESTO), guaranteeing security as long as not all servers are compromised at the same time. We prove our scheme secure in the UC framework which is known to provide the best security guarantees for password-based primitives. The core of our protocol are two new primitives we introduce: partially-oblivious distributed PRFs and a class of distributed signature schemes. Both allow for non-interactive refreshing of the secret key material and tolerate adaptive corruptions. We give secure instantiations based on the gap one-more BDH and RSA assumption respectively, leading to a highly efficient 2-round PESTO protocol. We also present an implementation and benchmark of our scheme in Java, realizing OAuth-compatible bearer tokens for SSO, demonstrating the viability of our approach.

At CRYPTO 2018 Cramer et al. presented SPDZ2k , a new secret-sharing based protocol for actively secure multi-party computation against a dishonest majority, that works over rings instead of fields. Their protocol uses slightly more communication than competitive schemes working over fields. However, implementation-wise, their approach allows for arithmetic to be carried out using native 32 or 64-bit CPU operations rather than modulo a large prime. The authors thus conjectured that the increased communication would be more than made up for by the increased efficiency of implementations. In this work we answer their conjecture in the affirmative. We do so by implementing their scheme, and designing and implementing new efficient protocols for equality test, comparison, and truncation over rings. We further show that these operations find application in the machine learning domain, and indeed significantly outperform their field-based competitors. In particular, we implement and benchmark oblivious algorithms for decision tree and support vector machine (SVM) evaluation.

We present two new, highly efficient, protocols for securely generating a distributed RSA key pair in the two-party setting. One protocol is semi-honestly secure and the other maliciously secure. Both are constant round and do not rely on any specific number-theoretic assumptions and improve significantly over the state-of-the-art by allowing a slight leakage (which we show to not affect security). For our maliciously secure protocol our most significant improvement comes from executing most of the protocol in a “strong” semi-honest manner and then doing a single, light, zero-knowledge argument of correct execution. We introduce other significant improvements as well. One such improvement arrives in showing that certain, limited leakage does not compromise security, which allows us to use lightweight subprotocols. Another improvement, which may be of independent interest, comes in our approach for multiplying two large integers using OT, in the malicious setting, without being susceptible to a selective-failure attack. Finally, we implement our malicious protocol and show that its performance is an order of magnitude better than the best previous protocol, which provided only semi-honest security. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.