Deploying Secure Multi-Party Computation for Financial Data Analysis - (Short Paper)

By Dan Bogdanov, Riivo Talviste, Jan Willemson. 2012

113 citations recorded by the source corpus.

Subfields: Privacy & Security, Mathematical Optimization

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The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Privacy & Security

In Mathematical Optimization

Citations in the corpus

High-performance secure multi-party computation for data mining applications (2012, background): “Since then, the practical feasibility of MPC has also been shown for network anomaly detection [7] and joint analysis of financial data [5].”
ABY3: A Mixed Protocol Framework for Machine Learning (2018, methodology): “In the three-party computation (3PC) setting with one corruption, following up on a large body of work [12, 13, 22, 38], new techniques and implementa-”
SoK: General Purpose Compilers for Secure Multi-Party Computation (2019, background): “broad potential for practical applications, ranging from general computations of secure statistical analysis [22], [23], [25], [52], [53], [54], [55], [96], to more domainspecific uses like financial oversight [2], [21], [26], [58], biomedical computations [35], [32], [75], [84], [82], [85], [132] a”
Trident: Efficient 4PC Framework for Privacy Preserving Machine Learning (2019, background): “While MPC has been shown to be practical [7]– [9], MPC for a small number of parties in the honest majority setting [10]–[15], [15]–[19] has become popular over the last few years due to applications such as financial data analysis [20], email spam filtering [21], distributed credential encryption [”
Rmind: A Tool for Cryptographically Secure Statistical Analysis (2016, background): “Secure multi-party computation was considered impractical for years, but clever protocol design and determined engineering have led to first real-world applications [12, 11].”
zkLedger: Privacy-Preserving Auditing for Distributed Ledgers (2018, background): “Previous work proposed a multi-party computation scheme in which participants use a secure protocol to compute the results of functions which answer questions about systemic financial risk, the same problem which zkLedger aims to address [3, 10], and network security [14].”
On Deploying Secure Computing Commercially: Private Intersection-Sum Protocols and their Business Applications (2019, background): “Others include a financial application in Estonia [6], and a secure survey of faculty salaries at universities in Massachusetts [36].”
Privacy-Preserving Data Processing with Flexible Access Control (2020, unknown): “Several schemes [26], [27]”
Encrypted data processing with Homomorphic Re-Encryption (2017, methodology): “Its multiplication operation is similar to Sharemind, which is achieved with 3 N multiplications of shared secrets.”