Level up: Private non-interactive decision tree evaluation using levelled homomorphic encryption
R Akhavan Mahdavi, H Ni, D Linkov… - Proceedings of the 2023 …, 2023 - dl.acm.org
As machine learning as a service continues gaining popularity, concerns about privacy and
intellectual property arise. Users often hesitate to disclose their private information to obtain …
intellectual property arise. Users often hesitate to disclose their private information to obtain …
Privacy-preserving tree-based inference with fully homomorphic encryption
Privacy enhancing technologies (PETs) have been proposed as a way to protect the privacy
of data while still allowing for data analysis. In this work, we focus on Fully Homomorphic …
of data while still allowing for data analysis. In this work, we focus on Fully Homomorphic …
Towards Private Deep Learning-Based Side-Channel Analysis Using Homomorphic Encryption: Opportunities and Limitations
This work investigates using Homomorphic Encryption (HE) to assist the security evaluation
of cryptographic devices without revealing side-channel information. For the first time, we …
of cryptographic devices without revealing side-channel information. For the first time, we …
Morphling: A Throughput-Maximized TFHE-based Accelerator using Transform-domain Reuse
A Putra, JY Kim - 2024 IEEE International Symposium on …, 2024 - ieeexplore.ieee.org
Fully Homomorphic Encryption (FHE) has become an increasingly important aspect in
modern computing, particularly in preserving privacy in cloud computing by enabling …
modern computing, particularly in preserving privacy in cloud computing by enabling …
Privacy-Preserving Tree-Based Inference with TFHE
Abstract Fully Homomorphic Encryption is a powerful tool for processing encrypted data and
is particularly adapted to the type of programs that are common in machine learning (ML) …
is particularly adapted to the type of programs that are common in machine learning (ML) …
[图书][B] High Performance Privacy Preserving AI
Artificial intelligence (AI) depends on data. In sensitive domains–such as healthcare,
security, finance, and many more–there is therefore a tension between unleashing the …
security, finance, and many more–there is therefore a tension between unleashing the …
Tyche: Probabilistic Selection over Encrypted Data for Generative Language Models
L Folkerts, NG Tsoutsos - Cryptology ePrint Archive, 2024 - eprint.iacr.org
Generative AI, a significant technological disruptor in recent years, has impacted domains
like augmented reality, coding assistance, and text generation. However, use of these …
like augmented reality, coding assistance, and text generation. However, use of these …
Model Stealing Attacks On FHE-based Privacy-Preserving Machine Learning through Adversarial Examples
Classic MLaaS solutions suffer from privacy-related risks since the user is required to send
unencrypted data to the server hosting the MLaaS. To alleviate this problem, a thriving line …
unencrypted data to the server hosting the MLaaS. To alleviate this problem, a thriving line …
Privacy-Enhancing Technologies
IA Álvarez, M Ehaus, ML Frank, J Sedlmeir - … : Financial Sector in Change, 2024 - Springer
In the course of the digital transformation, increasing amounts of data are exchanged
digitally. Business processes and regulatory compliance requirements that demand …
digitally. Business processes and regulatory compliance requirements that demand …
On the Security of Privacy-Preserving Machine Learning Against Model Stealing Attacks
Abstract Classic Machine Learning as a Service (MLaaS) solutions suffer from privacy-
related risks since the user inputs are sent in the clear to the server hosting the ML model …
related risks since the user inputs are sent in the clear to the server hosting the ML model …