fully-homomorphic-encryption

The FHE Team at Google, in partnership with multiple collaborators, is working to Unlock Fully Homomorphic Encryption for Google and the world. Make it Easy Make it Fast at Scale What started with a C++ transpiler, has morphed into two new libraries: HEIR is the new development platform and...

fully-homomorphic-encryption

فريق FHE في Google، بالتعاون مع العديد من الشركاء، يعمل على إطلاق التشفير المتماثل بالكامل لـ Google والعالم. اجعله سهلاً اجعله سريعاً على نطاق واسع ما بدأ كمترجم transpiler بلغة C++، تحول إلى مكتبتين جديدتين: HEIR هي منصة التطوير الجديدة وسلسلة أدوات المترجم compiler toolchain لتحويل النماذج...

HElib

HElib HElib est une bibliothèque logicielle open source licence Apache v2.0 qui implémente le chiffrement homomorphe CH. Les schémas actuellement disponibles sont les implémentations du schéma Brakerski-Gentry-Vaikuntanathan BGV avec bootstrapping et du schéma à nombres approximatifs de...

Static Bootstrap Placement for Encrypted Language Model Decoding

Language models increasingly serve prompts that carry private data, and secure inference under homomorphic encryption lets a client outsource the computation without revealing the prompt. Existing secure inference systems run a forward pass without consuming a token under encryption, and generati...

Homomorphic Advantage Operator: Stabilizing Reinforcement Learning under Fully Homomorphic Encryption Constraints

Privacy-preserving machine learning presents significant deployment challenges on the cloud for intelligent systems with confidential data. Fully Homomorphic Encryption FHE offers a compelling solution for secure computation, preserving data confidentiality of cloud computations. However, applyin...

Implementing Homomorphic Encryption-Based Logic Locking in System-On-Chip Designs

This study presents a logic locking scheme based on the binary Ring Learning With Errors algorithm, implemented in a RISC-V System-on-Chip design. Unlike traditional logic locking methods that require providing users with raw locking parameters, the proposed approach secures critical logic paths ...

fully-homomorphic-encryption helrm

The FHE Team at Google, in partnership with multiple collaborators, is working to Unlock Fully Homomorphic Encryption for Google and the world. Make it Easy Make it Fast at Scale What started with a C++ transpiler, has morphed into two new libraries: HEIR is the new development platform and...

Safety and Security: Experimental Validation of Encrypted Model Predictive Control

In this paper, we revisit the problem of an encrypted model predictive control MPC design, representing a significant challenge in the recent field of secure process control. Existing methods in secure optimization-based control are non-existent and even partial implementation fails to address th...

Federated Learning Architecture: Data Privacy and System Security Approaches

This study explores the integration of homomorphic encryption and differential privacy techniques to enhance data privacy and security in Federated Learning FL systems. FL allows data to remain on local devices, eliminating the need for centralized data collection; however, sensitive information...

Protecting On-Device AI Inference: A Systematic Review of Attacks and Defence Mechanisms

The need for secure and private Artificial Intelligence AI and Machine Learning ML on edge and mobile devices has increased the necessity of protecting the architecture of these systems from threats to both security and privacy. With an ever-increasing number of pre-trained AI models being used o...

Encrypted Neural Networks without Overflows

Fully homomorphic encryption FHE enables private inference by evaluating neural networks on encrypted data. In this way, we can delegate the computation to a third party server without ever revealing the user's data. Currently, the CKKS scheme is the backbone of most efficient FHE implementations...

On the (Non-)Resilience of Encrypted Controllers to Covert Attacks

The security of networked control systems NCS is receiving increasing attention from both cyber-security and system-theoretic perspectives. The former focuses on classical IT security goals such as confidentiality, integrity, and availability of process data, while the latter investigates tailore...

Towards Secure Retrieval-Augmented Generation: A Comprehensive Review of Threats, Defenses and Benchmarks

Retrieval-Augmented Generation RAG significantly mitigates the hallucinations and domain knowledge deficiency in large language models by incorporating external knowledge bases. However, the multi-module architecture of RAG introduces complex system-level security vulnerabilities. Guided by the R...

DRAMatic Speedup: Accelerating HE Operations on a Processing-In-Memory System

Homomorphic encryption HE is a promising technology for confidential cloud computing, as it allows computations on encrypted data. However, HE is computationally expensive and often memory-bound on conventional computer architectures. Processing-in-Memory PIM is an alternative hardware architectu...

Secrecy and Verifiability: An Introduction to Electronic Voting

Democracies are built upon secure and reliable voting systems. Electronic voting systems seek to replace ballot papers and boxes with computer hardware and software. Proposed electronic election schemes have been subjected to scrutiny, with researchers spotting inherent faults and weaknesses...

Efficient Decoding Methods for Language Models on Encrypted Data

Large language models LLMs power modern AI applications, but processing sensitive data on untrusted servers raises privacy concerns. Homomorphic encryption HE enables computation on encrypted data for secure inference. However, neural text generation requires decoding methods like argmax and...

PP-STAT: an Efficient Privacy-Preserving Statistical Analysis Framework Using Homomorphic Encryption

With the widespread adoption of cloud computing, the need for outsourcing statistical analysis to third-party platforms is growing rapidly. However, handling sensitive data such as medical records and financial information in cloud environments raises serious privacy concerns. In this paper, we...

Activate Me!: Designing Efficient Activation Functions for Privacy-Preserving Machine Learning with Fully Homomorphic Encryption

The growing adoption of machine learning in sensitive areas such as healthcare and defense introduces significant privacy and security challenges. These domains demand robust data protection, as models depend on large volumes of sensitive information for both training and inference. Fully...

HEIR: a Universal Compiler for Homomorphic Encryption

This work presents Homomorphic Encryption Intermediate Representation HEIR, a unified approach to building homomorphic encryption HE compilers. HEIR aims to support all mainstream techniques in homomorphic encryption, integrate with all major software libraries and hardware accelerators, and...

SenseCrypt: Sensitivity-Guided Selective Homomorphic Encryption for Joint Federated Learning in Cross-Device Scenarios

Homomorphic Encryption HE prevails in securing Federated Learning FL, but suffers from high overhead and adaptation cost. Selective HE methods, which partially encrypt model parameters by a global mask, are expected to protect privacy with reduced overhead and easy adaptation. However, in...