Can One Safety Loop Guard Them All? Agentic Guard Rails for Federated Computing

We propose Guardian-FC, a novel two-layer framework for privacy preserving federated computing that unifies safety enforcement across diverse privacy preserving mechanisms, including cryptographic back-ends like fully homomorphic encryption FHE and multiparty computation MPC, as well as statistic...

Secure Multi-Key Homomorphic Encryption with Application to Privacy-Preserving Federated Learning

Whitepaper called Secure Multi-Key Homomorphic Encryption With Application To Privacy-Preserving Federated Learning...

Accurate BGV Parameters Selection: Accounting for Secret and Public Key Dependencies in Average-Case Analysis

The Brakerski-Gentry-Vaikuntanathan BGV scheme is one of the most significant fully homomorphic encryption FHE schemes. It belongs to a class of FHE schemes whose security is based on the presumed intractability of the Learning with Errors LWE problem and its ring variant RLWE. Such schemes deal...

Cut Tracing with E-Graphs for Boolean FHE Circuit Synthesis

Fully Homomorphic Encryption FHE is a promising privacy-preserving technology enabling secure computation over encrypted data. A major limitation of current FHE schemes is their high runtime overhead. As a result, automatic optimization of circuits describing FHE computation has garnered...

Bidirectional Biometric Authentication Using Transciphering and (T)FHE

Biometric authentication systems pose privacy risks, as leaked templates such as iris or fingerprints can lead to security breaches. Fully Homomorphic Encryption FHE enables secure encrypted evaluation, but its deployment is hindered by large ciphertexts, high key overhead, and limited trust...

Leveraging Photonic Interconnects for Scalable and Efficient Fully Homomorphic Encryption

Fully Homomorphic Encryption FHE facilitates secure computations on encrypted data but imposes significant demands on memory bandwidth and computational power. While current FHE accelerators focus on optimizing computation, they often face bandwidth limitations that result in performance...

HE-LRM: Encrypted Deep Learning Recommendation Models Using Fully Homomorphic Encryption

Fully Homomorphic Encryption FHE is an encryption scheme that not only encrypts data but also allows for computations to be applied directly on the encrypted data. While computationally expensive, FHE can enable privacy-preserving neural inference in the client-server setting: a client encrypts...

FicGCN: Unveiling the Homomorphic Encryption Efficiency from Irregular Graph Convolutional Networks

Graph Convolutional Neural Networks GCNs have gained widespread popularity in various fields like personal healthcare and financial systems, due to their remarkable performance. Despite the growing demand for cloud-based GCN services, privacy concerns over sensitive graph data remain significant...

Secure Distributed Learning for CAVs: Defending against Gradient Leakage with Leveled Homomorphic Encryption

Federated Learning FL enables collaborative model training across distributed clients without sharing raw data, making it a promising approach for privacy-preserving machine learning in domains like Connected and Autonomous Vehicles CAVs. However, recent studies have shown that exchanged model...

Understanding the Error Sensitivity of Privacy-Aware Computing

Homomorphic Encryption HE enables secure computation on encrypted data without decryption, allowing a great opportunity for privacy-preserving computation. In particular, domains such as healthcare, finance, and government, where data privacy and security are of utmost importance, can benefit fro...

Hermes: High-Performance Homomorphically Encrypted Vector Databases

Whitepaper called Hermes: High-Performance Homomorphically Encrypted Vector Databases...

FedShield-LLM: a Secure and Scalable Federated Fine-Tuned Large Language Model

Federated Learning FL offers a decentralized framework for training and fine-tuning Large Language Models LLMs by leveraging computational resources across organizations while keeping sensitive data on local devices. It addresses privacy and security concerns while navigating challenges associate...

Towards a DSL for Hybrid Secure Computation

Fully homomorphic encryption FHE and trusted execution environments TEE are two approaches to provide confidentiality during data processing. Each approach has its own strengths and weaknesses. In certain scenarios, computations can be carried out in a hybrid environment, using both FHE and TEE...

SHE-LoRA: Selective Homomorphic Encryption for Federated Tuning with Heterogeneous LoRA

Federated fine-tuning of large language models LLMs is critical for improving their performance in handling domain-specific tasks. However, prior work has shown that clients' private data can actually be recovered via gradient inversion attacks. Existing privacy preservation techniques against su...

Privacy-Aware Cyberterrorism Network Analysis Using Graph Neural Networks and Federated Learning

Cyberterrorism poses a formidable threat to digital infrastructures, with increasing reliance on encrypted, decentralized platforms that obscure threat actor activity. To address the challenge of analyzing such adversarial networks while preserving the privacy of distributed intelligence data, we...

Compile-Time Fully Homomorphic Encryption of Vectors: Eliminating Online Encryption Via Algebraic Basis Synthesis

Whitepaper called Compile-Time Fully Homomorphic Encryption Of Vectors: Eliminating Online Encryption Via Algebraic Basis Synthesis...

Outsourced Privacy-Preserving Feature Selection Based on Fully Homomorphic Encryption

Feature selection is a technique that extracts a meaningful subset from a set of features in training data. When the training data is large-scale, appropriate feature selection enables the removal of redundant features, which can improve generalization performance, accelerate the training process...

Side Channel Analysis in Homomorphic Encryption

Homomorphic encryption provides many opportunities for privacy-aware processing, including with methods related to machine learning. Many of our existing cryptographic methods have been shown in the past to be susceptible to side channel attacks. With these, the implementation of the cryptographi...

Quantized Approximate Signal Processing (QASP): Towards Homomorphic Encryption for Audio

Audio and speech data are increasingly used in machine learning applications such as speech recognition, speaker identification, and mental health monitoring. However, the passive collection of this data by audio listening devices raises significant privacy concerns. Fully homomorphic encryption...

Private Transformer Inference in MLaaS: a Survey

Transformer models have revolutionized AI, powering applications like content generation and sentiment analysis. However, their deployment in Machine Learning as a Service MLaaS raises significant privacy concerns, primarily due to the centralized processing of sensitive user data. Private...