MPCLeague: Robust MPC Platform for Privacy-Preserving Machine Learning

• URL: http://arxiv.org/abs/2112.13338v1
• Date: Sun, 26 Dec 2021 09:25:32 GMT
• Title: MPCLeague: Robust MPC Platform for Privacy-Preserving Machine Learning
• Authors: Ajith Suresh
• Abstract summary: This thesis focuses on designing efficient MPC frameworks for 2, 3 and 4 parties, with at most one corruption and supports ring structures. We propose two variants for each of our frameworks, with one variant aiming to minimise the execution time while the other focuses on the monetary cost.
• Score: 5.203329540700177
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In the modern era of computing, machine learning tools have demonstrated their potential in vital sectors, such as healthcare and finance, to derive proper inferences. The sensitive and confidential nature of the data in such sectors raises genuine concerns for data privacy. This motivated the area of Privacy-preserving Machine Learning (PPML), where privacy of data is guaranteed. In this thesis, we design an efficient platform, MPCLeague, for PPML in the Secure Outsourced Computation (SOC) setting using Secure Multi-party Computation (MPC) techniques. MPC, the holy-grail problem of secure distributed computing, enables a set of n mutually distrusting parties to perform joint computation on their private inputs in a way that no coalition of t parties can learn more information than the output (privacy) or affect the true output of the computation (correctness). While MPC, in general, has been a subject of extensive research, the area of MPC with a small number of parties has drawn popularity of late mainly due to its application to real-time scenarios, efficiency and simplicity. This thesis focuses on designing efficient MPC frameworks for 2, 3 and 4 parties, with at most one corruption and supports ring structures. At the heart of this thesis are four frameworks - ASTRA, SWIFT, Tetrad, ABY2.0 - catered to different settings. The practicality of our framework is argued through improvements in the benchmarking of widely used ML algorithms -- Linear Regression, Logistic Regression, Neural Networks, and Support Vector Machines. We propose two variants for each of our frameworks, with one variant aiming to minimise the execution time while the other focuses on the monetary cost. The concrete efficiency gains of our frameworks coupled with the stronger security guarantee of robustness make our platform an ideal choice for a real-time deployment of PPML techniques.

Related papers

• Low-Latency Privacy-Preserving Deep Learning Design via Secure MPC [31.35072624488929]
  Secure multi-party computation (MPC) facilitates privacy-preserving computation between multiple parties without leaking private information. This work proposes a low-latency secret-sharing-based MPC design that reduces unnecessary communication rounds during the execution of MPC protocols.
  arXiv  Detail & Related papers  (2024-07-24T07:01:21Z)
• The Security and Privacy of Mobile Edge Computing: An Artificial Intelligence Perspective [64.36680481458868]
  Mobile Edge Computing (MEC) is a new computing paradigm that enables cloud computing and information technology (IT) services to be delivered at the network's edge. This paper provides a survey of security and privacy in MEC from the perspective of Artificial Intelligence (AI) We focus on new security and privacy issues, as well as potential solutions from the viewpoints of AI.
  arXiv  Detail & Related papers  (2024-01-03T07:47:22Z)
• Privacy Preserving Multi-Agent Reinforcement Learning in Supply Chains [5.436598805836688]
  This paper addresses privacy concerns in multiagent reinforcement learning (MARL) within the context of supply chains. We propose a game-theoretic, privacy-related mechanism, utilizing a secure multi-party computation framework in MARL settings. We present a learning mechanism that carries out floating point operations in a privacy-preserving manner.
  arXiv  Detail & Related papers  (2023-12-09T21:25:21Z)
• Federated Fine-Tuning of LLMs on the Very Edge: The Good, the Bad, the Ugly [62.473245910234304]
  This paper takes a hardware-centric approach to explore how Large Language Models can be brought to modern edge computing systems. We provide a micro-level hardware benchmark, compare the model FLOP utilization to a state-of-the-art data center GPU, and study the network utilization in realistic conditions.
  arXiv  Detail & Related papers  (2023-10-04T20:27:20Z)
• Libertas: Privacy-Preserving Computation for Decentralised Personal Data Stores [19.54818218429241]
  We propose a modular design for integrating Secure Multi-Party Computation with Solid. Our architecture, Libertas, requires no protocol level changes in the underlying design of Solid. We show how this can be combined with existing differential privacy techniques to also ensure output privacy.
  arXiv  Detail & Related papers  (2023-09-28T12:07:40Z)
• Is Vertical Logistic Regression Privacy-Preserving? A Comprehensive Privacy Analysis and Beyond [57.10914865054868]
  We consider vertical logistic regression (VLR) trained with mini-batch descent gradient. We provide a comprehensive and rigorous privacy analysis of VLR in a class of open-source Federated Learning frameworks.
  arXiv  Detail & Related papers  (2022-07-19T05:47:30Z)
• Distributed Reinforcement Learning for Privacy-Preserving Dynamic Edge Caching [91.50631418179331]
  A privacy-preserving distributed deep policy gradient (P2D3PG) is proposed to maximize the cache hit rates of devices in the MEC networks. We convert the distributed optimizations into model-free Markov decision process problems and then introduce a privacy-preserving federated learning method for popularity prediction.
  arXiv  Detail & Related papers  (2021-10-20T02:48:27Z)
• CrypTen: Secure Multi-Party Computation Meets Machine Learning [25.21435023269728]
  CrypTen is a software framework that exposes popular secure MPC primitives via abstractions common in modern machine-learning frameworks. This paper describes the design of CrypTen and measure its performance on state-of-the-art models for text classification, speech recognition, and image classification.
  arXiv  Detail & Related papers  (2021-09-02T14:36:55Z)
• Reconfigurable Intelligent Surface Assisted Mobile Edge Computing with Heterogeneous Learning Tasks [53.1636151439562]
  Mobile edge computing (MEC) provides a natural platform for AI applications. We present an infrastructure to perform machine learning tasks at an MEC with the assistance of a reconfigurable intelligent surface (RIS) Specifically, we minimize the learning error of all participating users by jointly optimizing transmit power of mobile users, beamforming vectors of the base station, and the phase-shift matrix of the RIS.
  arXiv  Detail & Related papers  (2020-12-25T07:08:50Z)
• SWIFT: Super-fast and Robust Privacy-Preserving Machine Learning [16.17280000789628]
  We propose SWIFT, a robust framework for a range of ML algorithms in SOC setting. SWIFT guarantees output delivery to the users irrespective of any adversarial behaviour. We demonstrate our framework's practical relevance by benchmarking popular ML algorithms.
  arXiv  Detail & Related papers  (2020-05-20T18:20:23Z)
• Local Differential Privacy based Federated Learning for Internet of Things [72.83684013377433]
  Internet of Vehicles (IoV) simulates a large variety of crowdsourcing applications such as Waze, Uber, and Amazon Mechanical Turk, etc. Users of these applications report the real-time traffic information to the cloud server which trains a machine learning model based on traffic information reported by users for intelligent traffic management. In this paper, we propose to integrate federated learning and local differential privacy (LDP) to facilitate the crowdsourcing applications to achieve the machine learning model.
  arXiv  Detail & Related papers  (2020-04-19T14:03:10Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.