A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
Topological data analysis classifies encrypted bits with success.
Machine learning on encrypted data has received a lot of attention thanks to recent breakthroughs in homomorphic encryption and secure multi-party computation. It allows outsourcing computation to untrusted servers without sacrificing privacy of sensitive data. We propose a practical framework to perform partially encr…
A method for data encryption makes data look identical to humans but misleading to machine learning.
Information that is stored in an encrypted format is, by definition, usually not amenable to statistical analysis or machine learning methods. In this paper we present detailed analysis of coordinate and accelerated gradient descent algorithms which are capable of fitting least squares and penalised ridge regression mo…
Cryptotree enables accurate predictions on encrypted data using Random Forests.
Improved CNN accuracy for encrypted data using approximate activation functions.
Glyph speeds up DNN training on encrypted data by 99%.
Machine learning methods are widely used for a variety of prediction problems. \emph{Prediction as a service} is a paradigm in which service providers with technological expertise and computational resources may perform predictions for clients. However, data privacy severely restricts the applicability of such services…
We present SEALion: an extensible framework for privacy-preserving machine learning with homomorphic encryption. It allows one to learn deep neural networks that can be seamlessly utilized for prediction on encrypted data. The framework consists of two layers: the first is built upon TensorFlow and SEAL and exposes sta…
We present two new statistical machine learning methods designed to learn on fully homomorphic encrypted (FHE) data. The introduction of FHE schemes following Gentry (2009) opens up the prospect of privacy preserving statistical machine learning analysis and modelling of encrypted data without compromising security con…
Securely analyzes survival data across multiple institutions without revealing individual patient records.
Recent advances in cryptography promise to enable secure statistical computation on encrypted data, whereby a limited set of operations can be carried out without the need to first decrypt. We review these homomorphic encryption schemes in a manner accessible to statisticians and machine learners, focusing on pertinent…
Post-Quantum Secure Federated DeFi for Inclusive Banking
Paper proposes efficient privacy-preserving matrix encryption for secure collaborative learning against malicious adversaries.
Modular FHE enables accurate Gaussian process predictions without data exposure.
Securely share encrypted data for machine learning training.
Paper proposes privacy-preserving learning for images, making them imperceptible to humans but recognizable by machines.
The cloud-based speech recognition/API provides developers or enterprises an easy way to create speech-enabled features in their applications. However, sending audios about personal or company internal information to the cloud, raises concerns about the privacy and security issues. The recognition results generated in …
InstaHide encrypts images for privacy in distributed learning.
TextHide secures language understanding tasks by adding minimal encryption.
Federated learning is a distributed learning method to train a shared model by aggregating the locally-computed gradient updates. In federated learning, bandwidth and privacy are two main concerns of gradient updates transmission. This paper proposes an end-to-end encrypted neural network for gradient updates transmiss…
Secure sum outperforms homomorphic encryption in collaborative deep learning.
Paper proposes a method to encrypt faces while maintaining visual similarity.
In this letter, as a proof of concept, we propose a deep learning-based approach to attack the chaos-based image encryption algorithm in \cite{guan2005chaos}. The proposed method first projects the chaos-based encrypted images into the low-dimensional feature space, where essential information of plain images has been …
Fully Homomorphic Encryption (FHE) refers to a set of encryption schemes that allow computations to be applied directly on encrypted data without requiring a secret key. This enables novel application scenarios where a client can safely offload storage and computation to a third-party cloud provider without having to t…
Paper proposes a faster federated learning method for logistic regression.
A simple encoder and complex decoder for secure image encryption and decryption.
Securely trains fair models using homomorphic encryption.
Efficiently preserves privacy in logistic regression for IoT data.
Paper addresses federated contextual bandits with encryption.
New defense method inspired by encryption improves visual classification accuracy.
Paper introduces privacy-preserving deep-learning service.
The goal of homomorphic encryption is to encrypt data such that another party can operate on it without being explicitly exposed to the content of the original data. We introduce an idea for a privacy-preserving transformation on natural language data, inspired by homomorphic encryption. Our primary tool is {\em obfusc…
AI tested on 10 math questions from research.
SharedMF uses secret sharing to protect privacy in distributed recommendation systems.
Secure SMMD enables data privacy in federated learning.
RENNs protect input privacy by rotating d-ary features.
SecureGBM securely trains GBM models across two parties without revealing data.
Recent work has explored how to train machine learning models which do not discriminate against any subgroup of the population as determined by sensitive attributes such as gender or race. To avoid disparate treatment, sensitive attributes should not be considered. On the other hand, in order to avoid disparate impact,…
Model compression techniques, such as pruning and quantization, are becoming increasingly important to reduce the memory footprints and the amount of computations. Despite model size reduction, achieving performance enhancement on devices is, however, still challenging mainly due to the irregular representations of spa…
Leveled Homomorphic Encryption (LHE) offers a potential solution that could allow sectors with sensitive data to utilize the cloud and securely deploy their models for remote inference with Deep Neural Networks (DNN). However, this application faces several obstacles due to the limitations of LHE. One of the main probl…
HDP-VFL hybridizes DP for VFL, reducing privacy costs.
Distributed learning across a coalition of organizations allows the members of the coalition to train and share a model without sharing the data used to optimize this model. In this paper, we propose new secure architectures that guarantee preservation of data privacy, trustworthy sequence of iterative learning and equ…
We survey distributed deep learning models for training or inference without accessing raw data from clients. These methods aim to protect confidential patterns in data while still allowing servers to train models. The distributed deep learning methods of federated learning, split learning and large batch stochastic gr…
The paper proposes a privacy-preserving algorithm for decentralized learning using public-key cryptography.
FleXOR trains fractional quantization for neural networks, improving accuracy and size.
SplitNN-driven Vertical Partitioning enables distributed learning from diverse data sources.
Deep-Lock secures DNN models with secret keys.