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Faster CryptoNets: Leveraging Sparsity for Real-World Encrypted Inference
[article]
2018
arXiv
pre-print
Preserved Fulltext
We present Faster CryptoNets, a method for efficient encrypted inference using neural networks. ...
We also show how privacy-safe training techniques can be used to reduce the overhead of encrypted inference for real-world datasets by leveraging transfer learning and differential privacy. ...
Contributions We propose Faster CryptoNets -a method for encrypted inference on the order of seconds. ...
arXiv:1811.09953v1
fatcat:67vqy4vwqvahvd6kqcxuzp23ki
SoK: Privacy-preserving Deep Learning with Homomorphic Encryption
[article]
2022
arXiv
pre-print
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2021 pre-print arXiv:2112.12855v1 fatcat:q26k3bkwonev5mjqot6jfekchi
With homomorphic encryption (HE) computation can be performed on encrypted data without revealing its content. ...
In this systematization of knowledge, we take an in-depth look at approaches that combine neural networks with HE for privacy preservation. ...
for real-world encrypted inference,”
Medical Association. ...
arXiv:2112.12855v2
fatcat:35nloctuj5ahlgnlze3u2mcgz4
CryptoNite: Revealing the Pitfalls of End-to-End Private Inference at Scale
[article]
2021
arXiv
pre-print
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The privacy concerns of providing deep learning inference as a service have underscored the need for private inference (PI) protocols that protect users' data and the service provider's model using cryptographic ...
Recently proposed PI protocols have achieved significant reductions in PI latency by moving the computationally heavy homomorphic encryption (HE) parts to an offline/pre-compute phase. ...
For instance, Faster-CryptoNets and HEMET have only low-degree polynomial activations and the former reduces the number of HE operations by introducing the sparsity in the network (using pruning and quantization ...
arXiv:2111.02583v1
fatcat:w5cft4qgvrcuhhcfo4nrk57xye
XONN: XNOR-based Oblivious Deep Neural Network Inference
[article]
2019
arXiv
pre-print
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Advancements in deep learning enable cloud servers to provide inference-as-a-service for clients. ...
State-of-the-art frameworks require one round of interaction between the client and the server for each layer of the neural network, whereas, XONN requires a constant round of interactions for any number ...
Acknowledgements We would like to thank the anonymous reviewers for their insightful comments. ...
arXiv:1902.07342v2
fatcat:gf5gyhmltbdq5gllo5lckw765y
Scalable privacy-preserving cancer type prediction with homomorphic encryption
[article]
2022
arXiv
pre-print
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In this work, we explore the challenges of privacy preserving cancer detection using a real-world dataset consisting of more than 2 million genetic information for several cancer types. ...
We develop a solution for privacy preserving cancer inference which first leverages the domain knowledge on somatic mutations to efficiently encode genetic mutations and then uses statistical tests for ...
In summary, to enable practical real-world private inference, we need the ability to compute on high-dimensional data in the encrypted domain with low latency and high throughput. ...
arXiv:2204.05496v1
fatcat:rby2isau4zglvkaclus7zewzge
Identifying and Exploiting Structures for Reliable Deep Learning
[article]
2021
arXiv
pre-print
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However, as recent works point out, these systems suffer from several issues that make them unreliable for use in the real world, including vulnerability to adversarial attacks (Szegedy et al. [248]), ...
The extraordinary performance of these systems often gives the impression that they can be used to revolutionise our lives for the better. ...
for deploying learning algorithms in the real world. ...
arXiv:2108.07083v1
fatcat:lducrn5tlfeqvpxevz6gukfvse
Compiler and runtime systems for homomorphic encryption and graph processing on distributed and heterogeneous architectures
2020
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Fully Homomorphic Encryption (FHE) refers to a set of encryption schemes that allow computations on encrypted data without requiring a secret key. ...
I present CHET, a domain-specific optimizing compiler, that is designed to make the task of programming DNN inference applications using FHE easier. ...
Gluon's BASP-style execution is on average ∼ 1.5× faster than its BSP-style execution for graph applications on real-world large-diameter graphs at scale. ...
doi:10.26153/tsw/10165
fatcat:cyejg4vtojgsfiwbrbqima6idm