Homomorphic Encryption News Today

Privacy solutions to train Machine Learning models

Crypto venture capitalists back Shiba Inu's new blockchain project with a $12 million investment.

The development team behind Dogecoin spinoff Shiba Inu has introduced a new tool for enhancing user privacy in the SHIB and Web 3 ecosystems. The innovation,…

Zoom in on traditional and novel ways to secure serverless computing, a growingly popular cloud infrastructure model for providing backend services on demand.

Homomorphic encryption (HE) has emerged as a pivotal technology for secure neural network inference (SNNI), offering privacy-preserving computations on encrypted data. Despite active developments in this field, HE-based SNNI frameworks are impeded by three inherent limitations. Firstly, they cannot evaluate non-linear functions such as (Formula presented.), the most widely adopted activation function in neural networks. Secondly, the permitted number of homomorphic operations on ciphertexts is b...

Protecting sensitive data as Cloud computings growth soars

Lawmakers and vendors are set to usher in an era of mutualized data insights. Are banks ready?

Cryptocurrencies like Bitcoin have transformed the world of finance with their decentralized and pseudonymous nature. However, while Bitcoin offers a

Mobile crowdsensing (MCS) combined with federated learning, as an emerging data collection and intelligent process paradigm, has received lots of attention in social networks and mobile Internet-of-Things, etc. However, as the openness and transparent of mobile crowdsensing tasks, federated learning model and training samples for crowdsensing data still face enormous privacy revealing risks, and it will reduce the willingness of people or nodes to actively participate and provide data in MCS. In...

Electronic healthcare (e-healthcare) system has brought great convenience for people to seek medical treatment. However, data security, user privacy and online diagnosis efficiency have also aroused widespread public concern. In this paper, we propose an efficient and privacy-preserving online diagnosis scheme for e-healthcare system based on federated learning mechanism (FLM). Specifically, we first transform the data owner's data sharing problem into machine learning problem through using...