Completeness theorems for non-cryptographic fault-tolerant distributed computation

Ben-Or, Michael; Goldwasser, Shafi; Wigderson, Avi

doi:10.1145/3335741.3335756

Cited by 296 publications

(484 citation statements)

References 4 publications

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“…These n shares are then distributed between n parties. This process is called secret sharing (Ben-Or and Wigderson, 1988). We use a concrete example to better illustrate the secret sharing concept.…”

Section: Traditional Secure Mpcmentioning

confidence: 99%

FL-QSAR: a federated learning based QSAR prototype for collaborative drug discovery

Chen

Xue

Chuai

et al. 2020

Preprint

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Motivation: Quantitative structure-activity relationship (QSAR) analysis is commonly used in drug discovery. Collaborations among pharmaceutical institutions can lead to a better performance in QSAR prediction, however, intellectual property and related financial interests remain substantially hindering inter-institutional collaborations in QSAR modeling for drug discovery. Results:For the first time, we verified the feasibility of applying the horizontal federated learning (HFL), which is a recently developed collaborative and privacy-preserving learning framework to perform QSAR analysis. A prototype platform of federated-learning-based QSAR modeling for collaborative drug discovery, i.e, FL-QSAR, is presented accordingly. We first compared the HFL framework with a classic privacy-preserving computation framework, i.e., secure multiparty computation (MPC) to indicate its difference from various perspective.Then we compared FL-QSAR with the public collaboration in terms of QSAR modeling. Our extensive experiments demonstrated that (1) collaboration by FL-QSAR outperforms a single client using only its private data, and (2) collaboration by FL-QSAR achieves almost the same performance as that of collaboration via cleartext learning algorithms using all shared information. Taking together, our results indicate that FL-QSAR under the HFL framework provides an efficient solution to break the barriers between pharmaceutical institutions in QSAR modeling, therefore promote the development of collaborative and privacy-preserving drug discovery with extendable ability to other privacy-related biomedical areas. Availability and implementation:The source codes of the federated learning simulation and FL-QSAR are available on the GitHub:https://github.com/bm2-lab/FL-QSAR

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Section: Traditional Secure Mpcmentioning

confidence: 99%

FL-QSAR: a federated learning based QSAR prototype for collaborative drug discovery

Chen

Xue

Chuai

et al. 2020

Preprint

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“…Then, it is enough that each worker adds share of A T B with share of C. Following this idea, we can use BGW algorithm to calculate any polynomials of the inputs, with at least 2t − 1 workers. For formal description see [4].…”

Section: ) Phase 3 -Reconstructionmentioning

confidence: 99%

Limited-Sharing Multi-Party Computation for Massive Matrix Operations

Nodehi

Maddah-Ali

2018

2018 IEEE International Symposium on Information Theory (ISIT)

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In this paper, we consider a secure multi-party computation problem (MPC), where the goal is to offload the computation of an arbitrary polynomial function of some massive private matrices to a network of workers. The workers are not reliable, some may collude to gain information about the input data. The system is initialized by sharing a (randomized) function of each input matrix to each server. Since the input matrices are massive, the size of each share is assumed to be at most 1/k fraction of the input matrix, for some k ∈ N. The objective is to minimize the number of workers needed to perform the computation task correctly, such that even if an arbitrary subset of t − 1 workers, for some t ∈ N, collude, they cannot gain any information about the input matrices. We propose a sharing scheme, called polynomial sharing, and show that it admits basic operations such as adding and multiplication of matrices, and transposing a matrix. By concatenating the procedures for basic operations, we show that any polynomial function of the input matrices can be calculated, subject to the problem constraints. We show that the proposed scheme can offer order-wise gain, in terms of number of workers needed, compared to the approaches formed by concatenation of job splitting and conventional MPC approaches.

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“…An important observation is that SSSS allows each party to locally compute linear combinations of secrets and public values. Firstly, let us define [x] i as the i th share of data item, x. SSSS allows parties to locally compute the following arithmetic operations over their shares [6]: [4]. As opposed to entirely local computation for addition of shares and the addition and multiplication of a share with a constant value, multiplying shares necessitates one round of communication.…”

Section: Mpc From Arithmetic Secretmentioning

confidence: 99%

Privacy-Enhancing Fall Detection from Remote Sensor Data Using Multi-Party Computation

Mainali¹,

Shepherd²

2019

Proceedings of the 14th International Conference on Availability, Reliability and Security

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Motion-based fall detection systems are concerned with detecting falls from vulnerable users, which is typically performed by classifying measurements from a body-worn inertial measurement unit (IMU) using machine learning. Such systems, however, necessitate the collection of high-resolution measurements that may violate users' privacy, such as revealing their gait, activities of daily living (ADLs), and relative position using dead reckoning. In this paper, we investigate the application of multi-party computation (MPC) to IMU-based fall detection for protecting device measurement confidentiality. Our system is evaluated in a cloud-based setting that precludes parties from learning the underlying data using multiple, disparate cloud instances deployed in three geographical configurations. Using a publicly-available dataset, we demonstrate that MPC-based fall detection from IMU measurements is practical while achieving state-of-the-art error rates. In the best case, our system executes in 365.2 milliseconds, which falls well within the required time window for on-device data acquisition (750ms). CCS CONCEPTS• Security and privacy → Security services; Distributed systems security; Domain-specific security and privacy architectures; • Computer systems organization → Cloud computing.

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Completeness theorems for non-cryptographic fault-tolerant distributed computation

Cited by 296 publications

References 4 publications

FL-QSAR: a federated learning based QSAR prototype for collaborative drug discovery

FL-QSAR: a federated learning based QSAR prototype for collaborative drug discovery

Limited-Sharing Multi-Party Computation for Massive Matrix Operations

Privacy-Enhancing Fall Detection from Remote Sensor Data Using Multi-Party Computation

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