Privacy-Preserving Network Analytics

Hastings, Marcella; Falk, Brett Hemenway; Tsoukalas, Gerry

doi:10.2139/ssrn.3680000

Cited by 6 publications

(3 citation statements)

References 69 publications

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“…Prior usage of MPC in financial applications has mainly focused on auctions, such as [5,8,9,29], for one shot auctions, and [4,13,14] for auctions running in Dark Markets. MPC was also used for privacy-preserving financial data analysis, such as [7] to conduct statistics over the performance of companies throughout the year or to compute the systemic risk between financial institutions such as in [2] and [25]. Also, [30] and [19] used MPC for detecting fraud between financial institutions, and [11] used MPC for privacy-preserving federated learning for financial applications.…”

Section: Introductionmentioning

confidence: 99%

Private Liquidity Matching Using MPC

Atapoor

Smart

Alaoui

2022

Lecture Notes in Computer Science

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Many central banks, as well as blockchain systems, are looking into distributed versions of interbank payment systems, in particular the netting procedure. When executed in a distributed manner this presents a number of privacy problems. This paper studies a privacypreserving netting protocol to solve the gridlock resolution problem in such Real Time Gross Settlement systems. Our solution utilizes Multiparty Computation and is implemented in the SCALE MAMBA system, using Shamir secret sharing scheme over three parties in an actively secure manner. Our experiments show that, even for large throughput systems, such a privacy-preserving operation is often feasible.

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Section: Introductionmentioning

confidence: 99%

Private Liquidity Matching Using MPC

Atapoor

Smart

Alaoui

2022

Lecture Notes in Computer Science

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“…: Graph analysis techniques are nowadays a crucial tool for financial institutions, serving as a means to identify fraudulent bank accounts, or fraudulent or suspicious transactions (such as transactions related to money laundering). See [24] for a discussion on secure computation technologies applied to financial intelligence sharing, or [15] for a specific example of secure graph analysis for financial stress testing. These techniques help to extract features from a large amount of data, consisting of, for example, money movements between bank accounts.…”

Section: Introductionmentioning

confidence: 99%

Secure Fast Evaluation of Iterative Methods: With an Application to Secure PageRank

Cozzo

Smart

Alaoui

2021

Topics in Cryptology – CT-RSA 2021

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Iterative methods are a standard technique in many areas of scientific computing. The key idea is that a function is applied repeatedly until the resulting sequence converges to the correct answer. When applying such methods in a secure computation methodology (for example using MPC, FHE, or SGX) one either needs to perform enough steps to ensure convergence irrespective of the input data, or one needs to perform a convergence test within the algorithm, and this itself leads to a leakage of data. Using the Banach Fixed Point theorem, and its extensions, we show that this data-leakage can be quantified. We then apply this to a secure (via MPC) implementation of the PageRank methodology. For PageRank we show that allowing this small amount of data-leakage produces a much more efficient secure implementation, and that for many underlying graphs this 'leakage' is already known to any attacker.

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“…In settings without any bankruptcy costs, incentives can completely disappear, as shown byRogers & Veraart (2013).37 In addition, the financial organizations involved may be concerned about keeping information about their trading positions and partners private. SeeHastings, Hemenway Falk & Tsoukalas (2020) for a discussion of some related issues, and new methods of obtaining critical network information while preserving privacy.38 SeeAnderson, Erol & Ordoñez (2020) for an analysis showing how growth in shadow banking interacts with interventions within the banking sector, and can exacerbate risk.…”

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confidence: 99%

Systemic Risk in Financial Networks: A Survey

Jackson

Pernoud

2020

SSRN Journal

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We provide an overview of the relationship between financial networks and systemic risk. We present a taxonomy of different types of systemic risk, differentiating between direct externalities between financial organizations (e.g., defaults, correlated portfolios and firesales), and perceptions and feedback effects (e.g., bank runs, credit freezes). We also discuss optimal regulation and bailouts, measurements of systemic risk and financial centrality, choices by banks' regarding their portfolios and partnerships, and the changing nature of financial networks.

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Privacy-Preserving Network Analytics

Cited by 6 publications

References 69 publications

Private Liquidity Matching Using MPC

Private Liquidity Matching Using MPC

Secure Fast Evaluation of Iterative Methods: With an Application to Secure PageRank

Systemic Risk in Financial Networks: A Survey

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