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Documents authored by Chiang, James Hsin-yu


Document
FairPoS: Input Fairness in Permissionless Consensus

Authors: James Hsin-yu Chiang, Bernardo David, Ittay Eyal, and Tiantian Gong

Published in: LIPIcs, Volume 282, 5th Conference on Advances in Financial Technologies (AFT 2023)


Abstract
In permissionless consensus, the ordering of transactions or inputs in each block is freely determined by an anonymously elected block leader. A rational block leader will choose an ordering of inputs that maximizes financial gain; the emergence of automatic market makers in decentralized finance enables the block leader to front-run honest trade orders by injecting its own inputs prior to and after honest trades. Front-running is rampant in decentralized finance and reduces the utility of the system by extracting financial value from honest trades and increasing demand for block-space. Current proposals to prevent input order attacks by encrypting user inputs are not permissionless, as they rely on small static committees to perform distributed key generation and threshold decryption. Such committees require party authentication, knowledge of the number of participating parties or do not permit player replaceability and are therefore not permissionless. Moreover, alternative solutions based on sequencing inputs in order of their arrival cannot prevent front-running in an unauthenticated peer-2-peer network where message arrival is adversarially controlled. We present FairPoS, the first consensus protocol to achieve input fairness in the permissionless setting with security against adaptive adversaries in semi-synchronous networks. In FairPoS, the adversary cannot learn the plaintext of any client input before it is included in a block in the chain’s common-prefix. Thus, input ordering attacks that depend on observing pending client inputs in the clear are no longer possible. In FairPoS, this is achieved via Delay Encryption (DeFeo et al., EUROCRYPT 2021), a recent cryptographic primitive related to time-lock puzzles, allowing all client inputs in a given round to be encrypted under a key that can only be extracted after enough time has elapsed. In contrast to alternative approaches, the key extraction task in delay encryption can, in principle, be performed by any party in the permissionless setting and requires no distribution of secret key material amongst authenticated parties. However, key extraction requires highly specialized hardware in practice. Thus, FairPoS requires resource-rich staking parties to insert extracted keys into blocks, enabling light-clients to decrypt past inputs and relieving parties who join the execution from decrypting all inputs in the entire chain history. Realizing this in proof-of-stake is non-trivial; naive application of key extraction to proof-of-stake can result in chain stalls lasting the entire key extraction period. We overcome this challenge with a novel key extraction protocol, which tolerates adversarial delays in block delivery intended to prevent key extraction from completing on schedule. Critically, this also enables the adoption of a new longest-extendable-chain rule which allows FairPoS to achieve the same guarantees as Ouroborous Praos against an adaptive adversary.

Cite as

James Hsin-yu Chiang, Bernardo David, Ittay Eyal, and Tiantian Gong. FairPoS: Input Fairness in Permissionless Consensus. In 5th Conference on Advances in Financial Technologies (AFT 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 282, pp. 10:1-10:23, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)


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@InProceedings{chiang_et_al:LIPIcs.AFT.2023.10,
  author =	{Chiang, James Hsin-yu and David, Bernardo and Eyal, Ittay and Gong, Tiantian},
  title =	{{FairPoS: Input Fairness in Permissionless Consensus}},
  booktitle =	{5th Conference on Advances in Financial Technologies (AFT 2023)},
  pages =	{10:1--10:23},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-303-4},
  ISSN =	{1868-8969},
  year =	{2023},
  volume =	{282},
  editor =	{Bonneau, Joseph and Weinberg, S. Matthew},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/LIPIcs.AFT.2023.10},
  URN =		{urn:nbn:de:0030-drops-191990},
  doi =		{10.4230/LIPIcs.AFT.2023.10},
  annote =	{Keywords: Front-running, Delay Encryption, Proof-of-Stake, Blockchain}
}
Document
Correlated-Output Differential Privacy and Applications to Dark Pools

Authors: James Hsin-yu Chiang, Bernardo David, Mariana Gama, and Christian Janos Lebeda

Published in: LIPIcs, Volume 282, 5th Conference on Advances in Financial Technologies (AFT 2023)


Abstract
In the classical setting of differential privacy, a privacy-preserving query is performed on a private database, after which the query result is released to the analyst; a differentially private query ensures that the presence of a single database entry is protected from the analyst’s view. In this work, we contribute the first definitional framework for differential privacy in the trusted curator setting (Fig. 1); clients submit private inputs to the trusted curator, which then computes individual outputs privately returned to each client. The adversary is more powerful than the standard setting; it can corrupt up to n-1 clients and subsequently decide inputs and learn outputs of corrupted parties. In this setting, the adversary also obtains leakage from the honest output that is correlated with a corrupted output. Standard differentially private mechanisms protect client inputs but do not mitigate output correlation leaking arbitrary client information, which can forfeit client privacy completely. We initiate the investigation of a novel notion of correlated-output differential privacy to bound the leakage from output correlation in the trusted curator setting. We define the satisfaction of both standard and correlated-output differential privacy as round differential privacy and highlight the relevance of this novel privacy notion to all application domains in the trusted curator model. We explore round differential privacy in traditional "dark pool" market venues, which promise privacy-preserving trade execution to mitigate front-running; privately submitted trade orders and trade execution are kept private by the trusted venue operator. We observe that dark pools satisfy neither classic nor correlated-output differential privacy; in markets with low trade activity, the adversary may trivially observe recurring, honest trading patterns, and anticipate and front-run future trades. In response, we present the first round differentially private market mechanisms that formally mitigate information leakage from all trading activity of a user. This is achieved with fuzzy order matching, inspired by the standard randomized response mechanism; however, this also introduces a liquidity mismatch as buy and sell orders are not guaranteed to execute pairwise, thereby weakening output correlation; this mismatch is compensated for by a round differentially private liquidity provider mechanism, which freezes a noisy amount of assets from the liquidity provider for the duration of a privacy epoch, but leaves trader balances unaffected. We propose oblivious algorithms for realizing our proposed market mechanisms with secure multi-party computation (MPC) and implement these in the Scale-Mamba Framework using Shamir Secret Sharing based MPC. We demonstrate practical, round differentially private trading with comparable throughput as prior work implementing (traditional) dark pool algorithms in MPC; our experiments demonstrate practicality for both traditional finance and decentralized finance settings.

Cite as

James Hsin-yu Chiang, Bernardo David, Mariana Gama, and Christian Janos Lebeda. Correlated-Output Differential Privacy and Applications to Dark Pools. In 5th Conference on Advances in Financial Technologies (AFT 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 282, pp. 11:1-11:23, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)


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@InProceedings{chiang_et_al:LIPIcs.AFT.2023.11,
  author =	{Chiang, James Hsin-yu and David, Bernardo and Gama, Mariana and Lebeda, Christian Janos},
  title =	{{Correlated-Output Differential Privacy and Applications to Dark Pools}},
  booktitle =	{5th Conference on Advances in Financial Technologies (AFT 2023)},
  pages =	{11:1--11:23},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-303-4},
  ISSN =	{1868-8969},
  year =	{2023},
  volume =	{282},
  editor =	{Bonneau, Joseph and Weinberg, S. Matthew},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/LIPIcs.AFT.2023.11},
  URN =		{urn:nbn:de:0030-drops-192003},
  doi =		{10.4230/LIPIcs.AFT.2023.11},
  annote =	{Keywords: Differential Privacy, Secure Multi-party Computation, Dark Pools, Decentralized Finance}
}
Document
SoK: Privacy-Enhancing Technologies in Finance

Authors: Carsten Baum, James Hsin-yu Chiang, Bernardo David, and Tore Kasper Frederiksen

Published in: LIPIcs, Volume 282, 5th Conference on Advances in Financial Technologies (AFT 2023)


Abstract
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.

Cite as

Carsten Baum, James Hsin-yu Chiang, Bernardo David, and Tore Kasper Frederiksen. SoK: Privacy-Enhancing Technologies in Finance. In 5th Conference on Advances in Financial Technologies (AFT 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 282, pp. 12:1-12:30, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)


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@InProceedings{baum_et_al:LIPIcs.AFT.2023.12,
  author =	{Baum, Carsten and Chiang, James Hsin-yu and David, Bernardo and Frederiksen, Tore Kasper},
  title =	{{SoK: Privacy-Enhancing Technologies in Finance}},
  booktitle =	{5th Conference on Advances in Financial Technologies (AFT 2023)},
  pages =	{12:1--12:30},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-303-4},
  ISSN =	{1868-8969},
  year =	{2023},
  volume =	{282},
  editor =	{Bonneau, Joseph and Weinberg, S. Matthew},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/LIPIcs.AFT.2023.12},
  URN =		{urn:nbn:de:0030-drops-192019},
  doi =		{10.4230/LIPIcs.AFT.2023.12},
  annote =	{Keywords: DeFi, Anti-money laundering, MPC, FHE, identity management, PETs}
}
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