ARPA Whitepaper

We propose a secure computation solution for blockchain networks. The correctness of computation is verifiable even under malicious majority condition using information-theoretic Message Authentication Code (MAC), and the privacy is preserved using Secret-Sharing. With state-of-the-art multiparty computation protocol and a layer2 solution, our privacy-preserving computation guarantees data security on blockchain, cryptographically, while reducing the heavy-lifting computation job to a few nodes. This breakthrough has several implications on the future of decentralized networks. First, secure computation can be used to support Private Smart Contracts, where consensus is reached without exposing the information in the public contract. Second, it enables data to be shared and used in trustless network, without disclosing the raw data during data-at-use, where data ownership and data usage is safely separated. Last but not least, computation and verification processes are separated, which can be perceived as computational sharding, this effectively makes the transaction processing speed linear to the number of participating nodes. Our objective is to deploy our secure computation network as an layer2 solution to any blockchain system. Smart Contracts\cite{smartcontract} will be used as bridge to link the blockchain and computation networks. Additionally, they will be used as verifier to ensure that outsourced computation is completed correctly. In order to achieve this, we first develop a general MPC network with advanced features, such as: 1) Secure Computation, 2) Off-chain Computation, 3) Verifiable Computation, and 4)Support dApps' needs like privacy-preserving data exchange

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

Survey on securing data storage in the cloud

Cloud Computing has become a well-known primitive nowadays; many researchers and companies are embracing this fascinating technology with feverish haste. In the meantime, security and privacy challenges are brought forward while the number of cloud storage user increases expeditiously. In this work, we conduct an in-depth survey on recent research activities of cloud storage security in association with cloud computing. After an overview of the cloud storage system and its security problem, we focus on the key security requirement triad, i.e., data integrity, data confidentiality, and availability. For each of the three security objectives, we discuss the new unique challenges faced by the cloud storage services, summarize key issues discussed in the current literature, examine, and compare the existing and emerging approaches proposed to meet those new challenges, and point out possible extensions and futuristic research opportunities. The goal of our paper is to provide a state-of-the-art knowledge to new researchers who would like to join this exciting new field

Universally Composable Verifiable Random Oracles

Random Oracles werden häufig in der Kryptographie eingesetzt um sehr effiziente Instanziierungen mächtiger kryptographischer Primitive zu konstruieren. Jedoch ist diese Praxis im Allgemeinen nicht zulässig wie verschiedene Nicht-Instanziierungs-Ergebnisse für Random Oracles mittels lokal berechenbarer Familien von Funktionen durch Halevi et al. (JACM ’04) zeigt. Die Random Oracle Modell kann sicher eingesetzt werden, indem Random Oracles nicht mit einer lokal berechenbaren Hashfunktion, sondern stattdessen mit einem interaktiven Protokoll instanziiert werden. In der realen Welt könnte solch ein interaktives Protokoll beispielsweise aus einem vertrauenswürdigen Server, welcher über das Internet erreichbar ist, bestehen. Dieser Server würde sodann eine der bekannten Techniken wie lazy sampling oder das Auswerten einer Pseudo-Zufälligen Funktion verwenden, um die Funktionalität eines Random Oracle bereitzustellen. Ein klarer Nachteil dieses Ansatzes ist die große Menge an Interaktion, die bei jeder Berechnung, die eine Auswertung des Random Oracle beinhaltet, nötig ist. Wir wollen diese Interaktion auf ein Minimum reduzieren. Um obiges Unmöglichkeitsresultat zu umgehen, muss die Auswertung des Random Oracle auf einer frischen Eingabe Interaktion der auswertenden Partei mit einer anderen Partei beinhalten. Dies ist jedoch nicht der einzige Verwendungszweck von Random Oracles, der häufig in kryptographischen Protokollen auftritt. Bei einem weiteren solchen Zweck wertet zunächst eine Partei A das Orakel auf einer Eingabe aus und erhält einen Hashwert. Im Anschluss sendet A Eingabe und Ausgabe (im Kontext eines Protokolls) an eine zweite Partei B und möchte B davon überzeugen, dass das Random Oracle korrekt ausgewertet wurde. Eine einfache Möglichkeit dies zu prüfen besteht darin, dass B selbst eine Auswertung des Random Oracle auf der erhaltenen Eingabe tätigt und die beiden Ausgaben vergleicht. In unserem Kontext benötigt dies jedoch erneut Interaktion. Der Wunsch diesen zweiten Verwendungszweck nicht-interaktiv zu machen führt uns zum Begriff eines Verifiable Random Oracle (VRO) als Erweiterung eines Random Oracle. Abstrakt besteht ein VRO aus zwei Orakeln. Das erste Orakel verhält sich wie ein Random Oracle dessen Ausgabe um einen Korrektheitsbeweis erweitert wurde. Mit Hilfe dieses Beweises kann das zweite Orakel dazu verwendet werden öffentlich die korrekte Auswertung des Random Oracle zu verifizieren. Obwohl diese Orakel-basierte Formulierung nicht notwendigerweise nicht-interaktive Verifikation besitzt, so erlaubt jedoch die Einführung expliziter Korrektheitsbeweise dies. In dieser Masterarbeit formalisieren wir zunächst den Begriff eines VRO im Universal Composability Framework von Canetti (FOCS ’01). Danach wenden wir VROs auf zwei kryptographische Anwendungen an, die in ihrer ursprünglichen Formulierung das Random Oracle Modell verwenden, und zeigen, das deren Sicherheitseigenschaften erhalten bleiben. Um zu zeigen, dass unsere Definition realisierbar ist, konstruieren wir mehrere Protokolle, die die ideale VRO Funktionalität realisieren. Diese reichen von Protokollen für eine einzelne vertrauenswürdige Partei bis hin zu verteilten Protokollen, die eine gewisse Menge an böswilliger Korruption erlauben. Wir vergleichen weiterhin VROs mit ähnlichen existierenden Primitiven

Search Me If You Can: Privacy-preserving Location Query Service

Location-Based Service (LBS) becomes increasingly popular with the dramatic growth of smartphones and social network services (SNS), and its context-rich functionalities attract considerable users. Many LBS providers use users' location information to offer them convenience and useful functions. However, the LBS could greatly breach personal privacy because location itself contains much information. Hence, preserving location privacy while achieving utility from it is still an challenging question now. This paper tackles this non-trivial challenge by designing a suite of novel fine-grained Privacy-preserving Location Query Protocol (PLQP). Our protocol allows different levels of location query on encrypted location information for different users, and it is efficient enough to be applied in mobile platforms.Comment: 9 pages, 1 figure, 2 tables, IEEE INFOCOM 201

A comprehensive meta-analysis of cryptographic security mechanisms for cloud computing

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.The concept of cloud computing offers measurable computational or information resources as a service over the Internet. The major motivation behind the cloud setup is economic benefits, because it assures the reduction in expenditure for operational and infrastructural purposes. To transform it into a reality there are some impediments and hurdles which are required to be tackled, most profound of which are security, privacy and reliability issues. As the user data is revealed to the cloud, it departs the protection-sphere of the data owner. However, this brings partly new security and privacy concerns. This work focuses on these issues related to various cloud services and deployment models by spotlighting their major challenges. While the classical cryptography is an ancient discipline, modern cryptography, which has been mostly developed in the last few decades, is the subject of study which needs to be implemented so as to ensure strong security and privacy mechanisms in today’s real-world scenarios. The technological solutions, short and long term research goals of the cloud security will be described and addressed using various classical cryptographic mechanisms as well as modern ones. This work explores the new directions in cloud computing security, while highlighting the correct selection of these fundamental technologies from cryptographic point of view

Trustworthy Federated Learning: A Survey

Federated Learning (FL) has emerged as a significant advancement in the field of Artificial Intelligence (AI), enabling collaborative model training across distributed devices while maintaining data privacy. As the importance of FL increases, addressing trustworthiness issues in its various aspects becomes crucial. In this survey, we provide an extensive overview of the current state of Trustworthy FL, exploring existing solutions and well-defined pillars relevant to Trustworthy . Despite the growth in literature on trustworthy centralized Machine Learning (ML)/Deep Learning (DL), further efforts are necessary to identify trustworthiness pillars and evaluation metrics specific to FL models, as well as to develop solutions for computing trustworthiness levels. We propose a taxonomy that encompasses three main pillars: Interpretability, Fairness, and Security & Privacy. Each pillar represents a dimension of trust, further broken down into different notions. Our survey covers trustworthiness challenges at every level in FL settings. We present a comprehensive architecture of Trustworthy FL, addressing the fundamental principles underlying the concept, and offer an in-depth analysis of trust assessment mechanisms. In conclusion, we identify key research challenges related to every aspect of Trustworthy FL and suggest future research directions. This comprehensive survey serves as a valuable resource for researchers and practitioners working on the development and implementation of Trustworthy FL systems, contributing to a more secure and reliable AI landscape.Comment: 45 Pages, 8 Figures, 9 Table