Fully Distributed Verifiable Random Functions and their Application to Decentralised Random Beacons

Galindo, David; Liu, Jia; Ordean, Mihair; Wong, Jin-Mann

doi:10.1109/eurosp51992.2021.00017

Cited by 26 publications

(23 citation statements)

References 22 publications

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“…Nevertheless, these DRBs do not provide formal security analysis. A recent paper [36] provides two new constructions of DVRF: 1) DDH-DVRF based on elliptic curve cryptography; 2) GLOW-DVRF based on cryptographic pairings. These constructions also formalize a security model with proper security analysis.…”

Section: -Continuous Vdf [34]: Ephraim Et Al Presented a New Notion O...mentioning

confidence: 99%

“…VRF-based 1. Most of these DRBs do not have any trusted setup and achieve strong notion of pseudo-randomness and bias-resistance [36].…”

Section: Scheme Advantages Disadvantagesmentioning

confidence: 99%

“…DRB protocols can be constructed by employing different cryptographic primitives e.g. Publicly Verifiable Secret Sharing schemes (PVSS) [47,69,66,17,18,8,25], Threshold Crypto-Systems [16,45,31,20,55], Verifiable Random Functions (VRF) [27,39,36,71,22,24], Verifiable Delay Functions (VDF) [49,30,34,65,44]. Randomness can also be extracted from external data sources such as [7,21,13] or from the blockchain schemes having their own random beacon [47,36,45].…”

Section: Introductionmentioning

confidence: 99%

“…Publicly Verifiable Secret Sharing schemes (PVSS) [47,69,66,17,18,8,25], Threshold Crypto-Systems [16,45,31,20,55], Verifiable Random Functions (VRF) [27,39,36,71,22,24], Verifiable Delay Functions (VDF) [49,30,34,65,44]. Randomness can also be extracted from external data sources such as [7,21,13] or from the blockchain schemes having their own random beacon [47,36,45]. These DRB protocols are not equally-suited in all applications or use-cases due to the diversity in their designs, characteristics, and underlying assumptions.…”

Section: Introductionmentioning

confidence: 99%

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SoK: Decentralized Randomness Beacon Protocols

Raikwar¹,

Gligoroski²

2022

Preprint

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The scientific interest in the area of Decentralized Randomness Beacon (DRB) protocols has been thriving recently. Partially that interest is due to the success of the disruptive technologies introduced by modern cryptography, such as cryptocurrencies, blockchain technologies, and decentralized finances, where there is an enormous need for a public, reliable, trusted, verifiable, and distributed source of randomness. On the other hand, recent advancements in the development of new cryptographic primitives brought a huge interest in constructing a plethora of DRB protocols differing in design and underlying primitives. To the best of our knowledge, no systematic and comprehensive work systematizes and analyzes the existing DRB protocols. Therefore, we present a Systematization of Knowledge (SoK) intending to structure the multi-faced body of research on DRB protocols. In this SoK, we delineate the DRB protocols along the following axes: their underlying primitive, properties, and security. This SoK tries to fill that gap by providing basic standard definitions and requirements for DRB protocols, such as Unpredictability, Bias-resistance, Availability (or Liveness), and Public Verifiability. We classify DRB protocols according to the nature of interactivity among protocol participants. We also highlight the most significant features of DRB protocols such as scalability, complexity, and performance along with a brief discussion on its improvement. We present future research directions along with a few interesting research problems.

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Section: -Continuous Vdf [34]: Ephraim Et Al Presented a New Notion O...mentioning

confidence: 99%

“…VRF-based 1. Most of these DRBs do not have any trusted setup and achieve strong notion of pseudo-randomness and bias-resistance [36].…”

Section: Scheme Advantages Disadvantagesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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SoK: Decentralized Randomness Beacon Protocols

Raikwar¹,

Gligoroski²

2022

Preprint

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“…So this strategy is scalable and suitable for different applications. That is why there are many well-known public blockchain projects using the VRF as their randomness source, such as Algorand [72], Ouroboros Praos [112], and DVRFs [141]…”

Section: Vrfmentioning

confidence: 99%

Building Blocks of Sharding Blockchain Systems: Concepts, Approaches, and Open Problems

Liu¹,

Liu²,

Salles³

et al. 2021

Preprint

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Sharding is the prevalent approach to breaking the trilemma of simultaneously achieving decentralization, security, and scalability in traditional blockchain systems, which are implemented as replicated state machines relying on atomic broadcast for consensus on an immutable chain of valid transactions. Sharding is to be understood broadly as techniques for dynamically partitioning nodes in a blockchain system into subsets (shards) that perform storage, communication, and computation tasks without fine-grained synchronization with each other. Despite much recent research on sharding blockchains, much remains to be explored in the design space of these systems. Towards that aim, we conduct a systematic analysis of existing sharding blockchain systems and derive a conceptual decomposition of their architecture into functional components and the underlying assumptions about system models and attackers they are built on. The functional components identified are node selection, epoch randomness, node assignment, intra-shard consensus, cross-shard transaction processing, shard reconfiguration, and motivation mechanism. We describe interfaces, functionality, and properties of each component and show how they compose into a sharding blockchain system. For each component, we systematically review existing approaches, identify potential and open problems, and propose future research directions. We focus on potential security attacks and performance problems, including system throughput and latency concerns such as confirmation delays. We believe our modular architectural decomposition and in-depth analysis of each component, based on a comprehensive literature study, provides a systematic basis for conceptualizing state-of-the-art sharding blockchain systems, proving or improving security and performance properties of components, and developing new sharding blockchain system designs.

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Aggregatable Distributed Key Generation

Gurkan¹,

Jovanovic

Maller³

et al. 2021

Lecture Notes in Computer Science

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In this paper, we introduce a distributed key generation (DKG) protocol with aggregatable and publicly-verifiable transcripts. Compared with prior publicly-verifiable approaches, our DKG reduces the size of the final transcript and the time to verify it from O(n 2 ) to O(n log n), where n denotes the number of parties. As compared with prior non-publicly-verifiable approaches, our DKG leverages gossip rather than all-to-all communication to reduce verification and communication complexity. We also revisit existing DKG security definitions, which are quite strong, and propose new and natural relaxations. As a result, we can prove the security of our aggregatable DKG as well as that of several existing DKGs, including the popular Pedersen variant. We show that, under these new definitions, these existing DKGs can be used to yield secure threshold variants of popular cryptosystems such as El-Gamal encryption and BLS signatures. We also prove that our DKG can be securely combined with a new efficient verifiable unpredictable function (VUF), whose security we prove in the random oracle model. Finally, we experimentally evaluate our DKG and show that the perparty overheads scale linearly and are practical. For 64 parties, it takes 71 ms to share and 359 ms to verify the overall transcript, while for 8192 parties, it takes 8 s and 42.2 s respectively. cLabs, Ethereum Foundation.

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Fully Distributed Verifiable Random Functions and their Application to Decentralised Random Beacons

Cited by 26 publications

References 22 publications

SoK: Decentralized Randomness Beacon Protocols

SoK: Decentralized Randomness Beacon Protocols

Building Blocks of Sharding Blockchain Systems: Concepts, Approaches, and Open Problems

Aggregatable Distributed Key Generation

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