: Towards Secure and Lightweight Deep Learning as a Medical Diagnostic Service

Liu, Xiaoning; Zheng, Yifeng; Yuan, Xingliang; Yi, Xun

doi:10.1007/978-3-030-88418-5_25

Cited by 17 publications

(12 citation statements)

References 19 publications

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“…It could be semi-honest, which means the cloud server will honestly follow the protocol for the federated learning service, yet is curious about individual model updates so as to infer clients' local datasets. The semi-honest threat model is commonly adopted in privacypreserving data-centric services in cloud computing [27], [28]. For this setting our system aims to maintain the confidentiality of individual model updates.…”

Section: Threat Modelmentioning

confidence: 99%

Aggregation Service for Federated Learning: An Efficient, Secure, and More Resilient Realization

Zheng¹,

Lai²,

Liu³

et al. 2022

Preprint

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Federated learning has recently emerged as a paradigm promising the benefits of harnessing rich data from diverse sources to train high quality models, with the salient features that training datasets never leave local devices. Only model updates are locally computed and shared for aggregation to produce a global model. While federated learning greatly alleviates the privacy concerns as opposed to learning with centralized data, sharing model updates still poses privacy risks. In this paper, we present a system design which offers efficient protection of individual model updates throughout the learning procedure, allowing clients to only provide obscured model updates while a cloud server can still perform the aggregation. Our federated learning system first departs from prior works by supporting lightweight encryption and aggregation, and resilience against drop-out clients with no impact on their participation in future rounds. Meanwhile, prior work largely overlooks bandwidth efficiency optimization in the ciphertext domain and the support of security against an actively adversarial cloud server, which we also fully explore in this paper and provide effective and efficient mechanisms. Extensive experiments over several benchmark datasets (MNIST, CIFAR-10, and CelebA) show our system achieves accuracy comparable to the plaintext baseline, with practical performance.

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Section: Threat Modelmentioning

confidence: 99%

Aggregation Service for Federated Learning: An Efficient, Secure, and More Resilient Realization

Zheng¹,

Lai²,

Liu³

et al. 2022

Preprint

Self Cite

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“…In PrivGED, the power of the cloud is split into two cloud servers from different trust domains which can be hosted by different service providers in practice. Such multi-server model is getting increasingly popular in recent years for security designs in various domains, including both academia [32], [41][42][43][44][45][46][47] and industry [48], [49]. The adoption of such model in PrivGED follows this trend.…”

Section: Architecturementioning

confidence: 99%

“…What we need here essentially is secure comparison in the secret sharing domain. From the very recent works [18], [43], we identify two primitives suited to allow secure comparison in the secret sharing domain, and introduce two approaches accordingly to allow the realization of secure binning map generation. The first approach is based on FSS [18], which is more suited for high-latency network scenarios because it requires minimal rounds of interactions (at the cost of more local computation).…”

Section: Secure Binning Map Generationmentioning

confidence: 99%

“…The first approach is based on FSS [18], which is more suited for high-latency network scenarios because it requires minimal rounds of interactions (at the cost of more local computation). The second approach is based on ASS [43], which requires a small amount of local computation but higher online communication and round complexities, fitting better into low-latency network scenarios. FSS-based approach.…”

Section: Secure Binning Map Generationmentioning

confidence: 99%

“…ASS-based approach. This approach is based on the idea of secure bit decomposition in the secret sharing domain [43]. Specifically, given two fixed-point integers X, Y ∈ Z 2 k under two's complement representation, the most significant bit (MSB) of X − Y can indicate the relationship between X − Y and 0.…”

Section: Secure Binning Map Generationmentioning

confidence: 99%

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Privacy-Preserving Analytics on Decentralized Social Graphs: The Case of Eigendecomposition

Wang¹,

Zheng²,

Jia³

et al. 2022

Preprint

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Analytics over social graphs allows to extract valuable knowledge and insights for many fields like community detection, fraud detection, and interest mining. In practice, decentralized social graphs frequently arise, where the social graph is not available to a single entity and is decentralized among a large number of users, each holding only a limited local view about the whole graph. Collecting the local views for analytics of decentralized social graphs raises critical privacy concerns, as they encode private information about the social interactions among individuals. In this paper, we design, implement, and evaluate PrivGED, a new system aimed at privacy-preserving analytics over decentralized social graphs. PrivGED focuses on the support for eigendecomposition, one popular and fundamental graph analytics task producing eigenvalues/eigenvectors over the adjacency matrix of a social graph and benefits various practical applications. PrivGED is built from a delicate synergy of insights on graph analytics, lightweight cryptography, and differential privacy, allowing users to securely contribute their local views on a decentralized social graph for a cloud-based eigendecomposition analytics service while gaining strong privacy protection. Extensive experiments over real-world social graph datasets demonstrate that PrivGED achieves accuracy comparable to the plaintext domain, with practically affordable performance superior to prior art.

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A reputation-based and privacy-preserving incentive scheme for mobile crowd sensing: a deep reinforcement learning approach

Zhang

Shi

et al. 2022

Wireless Netw

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: Towards Secure and Lightweight Deep Learning as a Medical Diagnostic Service

Cited by 17 publications

References 19 publications

Aggregation Service for Federated Learning: An Efficient, Secure, and More Resilient Realization

Aggregation Service for Federated Learning: An Efficient, Secure, and More Resilient Realization

Privacy-Preserving Analytics on Decentralized Social Graphs: The Case of Eigendecomposition

A reputation-based and privacy-preserving incentive scheme for mobile crowd sensing: a deep reinforcement learning approach

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