SSNet: A Lightweight Multi-Party Computation Scheme for Practical Privacy-Preserving Machine Learning Service in the Cloud

• URL: http://arxiv.org/abs/2406.02629v1
• Date: Tue, 4 Jun 2024 00:55:06 GMT
• Title: SSNet: A Lightweight Multi-Party Computation Scheme for Practical Privacy-Preserving Machine Learning Service in the Cloud
• Authors: Shijin Duan, Chenghong Wang, Hongwu Peng, Yukui Luo, Wujie Wen, Caiwen Ding, Xiaolin Xu,
• Abstract summary: We propose SSNet, which for the first time employs Shamir's secret sharing (SSS) as the backbone of MPC-based ML framework. SSNet demonstrates the ability to scale up party numbers straightforwardly and embeds strategies to authenticate the computation correctness. We conduct comprehensive experimental evaluations on commercial cloud computing infrastructure from Amazon AWS.
• Score: 17.961150835215587
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: As privacy-preserving becomes a pivotal aspect of deep learning (DL) development, multi-party computation (MPC) has gained prominence for its efficiency and strong security. However, the practice of current MPC frameworks is limited, especially when dealing with large neural networks, exemplified by the prolonged execution time of 25.8 seconds for secure inference on ResNet-152. The primary challenge lies in the reliance of current MPC approaches on additive secret sharing, which incurs significant communication overhead with non-linear operations such as comparisons. Furthermore, additive sharing suffers from poor scalability on party size. In contrast, the evolving landscape of MPC necessitates accommodating a larger number of compute parties and ensuring robust performance against malicious activities or computational failures. In light of these challenges, we propose SSNet, which for the first time, employs Shamir's secret sharing (SSS) as the backbone of MPC-based ML framework. We meticulously develop all framework primitives and operations for secure DL models tailored to seamlessly integrate with the SSS scheme. SSNet demonstrates the ability to scale up party numbers straightforwardly and embeds strategies to authenticate the computation correctness without incurring significant performance overhead. Additionally, SSNet introduces masking strategies designed to reduce communication overhead associated with non-linear operations. We conduct comprehensive experimental evaluations on commercial cloud computing infrastructure from Amazon AWS, as well as across diverse prevalent DNN models and datasets. SSNet demonstrates a substantial performance boost, achieving speed-ups ranging from 3x to 14x compared to SOTA MPC frameworks. Moreover, SSNet also represents the first framework that is evaluated on a five-party computation setup, in the context of secure DL inference.

Related papers

• Digital Twin-Assisted Data-Driven Optimization for Reliable Edge Caching in Wireless Networks [60.54852710216738]
  We introduce a novel digital twin-assisted optimization framework, called D-REC, to ensure reliable caching in nextG wireless networks. By incorporating reliability modules into a constrained decision process, D-REC can adaptively adjust actions, rewards, and states to comply with advantageous constraints.
  arXiv  Detail & Related papers  (2024-06-29T02:40:28Z)
• Privacy preserving layer partitioning for Deep Neural Network models [0.21470800327528838]
  Trusted Execution Environments (TEEs) can introduce significant performance overhead due to additional layers of encryption, decryption, security and integrity checks. We introduce layer partitioning technique and offloading computations to GPU. We conduct experiments to demonstrate the effectiveness of our approach in protecting against input reconstruction attacks developed using trained conditional Generative Adversarial Network(c-GAN)
  arXiv  Detail & Related papers  (2024-04-11T02:39:48Z)
• RigorLLM: Resilient Guardrails for Large Language Models against Undesired Content [62.685566387625975]
  Current mitigation strategies, while effective, are not resilient under adversarial attacks. This paper introduces Resilient Guardrails for Large Language Models (RigorLLM), a novel framework designed to efficiently moderate harmful and unsafe inputs.
  arXiv  Detail & Related papers  (2024-03-19T07:25:02Z)
• MirrorNet: A TEE-Friendly Framework for Secure On-device DNN Inference [14.08010398777227]
  Deep neural network (DNN) models have become prevalent in edge devices for real-time inference. Existing defense approaches fail to fully safeguard model confidentiality or result in significant latency issues. This paper presents MirrorNet, which generates a TEE-friendly implementation for any given DNN model to protect the model confidentiality. For the evaluation, MirrorNet can achieve a 18.6% accuracy gap between authenticated and illegal use, while only introducing 0.99% hardware overhead.
  arXiv  Detail & Related papers  (2023-11-16T01:21:19Z)
• RRNet: Towards ReLU-Reduced Neural Network for Two-party Computation Based Private Inference [17.299835585861747]
  We introduce RRNet, a framework that aims to jointly reduce the overhead of MPC comparison protocols and accelerate computation through hardware acceleration. Our approach integrates the hardware latency of cryptographic building blocks into the DNN loss function, resulting in improved energy efficiency, accuracy, and security guarantees.
  arXiv  Detail & Related papers  (2023-02-05T04:02:13Z)
• PolyMPCNet: Towards ReLU-free Neural Architecture Search in Two-party Computation Based Private Inference [23.795457990555878]
  Secure multi-party computation (MPC) has been discussed, to enable the privacy-preserving deep learning (DL) computation. MPCs often come at very high computation overhead, and potentially prohibit their popularity in large scale systems. In this work, we develop a systematic framework, PolyMPCNet, of joint overhead reduction of MPC comparison protocol and hardware acceleration.
  arXiv  Detail & Related papers  (2022-09-20T02:47:37Z)
• An Adaptive Device-Edge Co-Inference Framework Based on Soft Actor-Critic [72.35307086274912]
  High-dimension parameter model and large-scale mathematical calculation restrict execution efficiency, especially for Internet of Things (IoT) devices. We propose a new Deep Reinforcement Learning (DRL)-Soft Actor Critic for discrete (SAC-d), which generates the emphexit point, emphexit point, and emphcompressing bits by soft policy iterations. Based on the latency and accuracy aware reward design, such an computation can well adapt to the complex environment like dynamic wireless channel and arbitrary processing, and is capable of supporting the 5G URL
  arXiv  Detail & Related papers  (2022-01-09T09:31:50Z)
• MPCLeague: Robust MPC Platform for Privacy-Preserving Machine Learning [5.203329540700177]
  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.
  arXiv  Detail & Related papers  (2021-12-26T09:25:32Z)
• 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)
• Multi-Exit Semantic Segmentation Networks [78.44441236864057]
  We propose a framework for converting state-of-the-art segmentation models to MESS networks. specially trained CNNs that employ parametrised early exits along their depth to save during inference on easier samples. We co-optimise the number, placement and architecture of the attached segmentation heads, along with the exit policy, to adapt to the device capabilities and application-specific requirements.
  arXiv  Detail & Related papers  (2021-06-07T11:37:03Z)
• CryptoSPN: Privacy-preserving Sum-Product Network Inference [84.88362774693914]
  We present a framework for privacy-preserving inference of sum-product networks (SPNs) CryptoSPN achieves highly efficient and accurate inference in the order of seconds for medium-sized SPNs.
  arXiv  Detail & Related papers  (2020-02-03T14:49:18Z)

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.