Search Results for "encryption using logistic map"

FATE (Federated AI Technology Enabler) is the world's first industrial grade federated learning open source framework to enable enterprises and institutions to collaborate on data while protecting data security and privacy. It implements secure computation protocols based on homomorphic encryption and multi-party computation (MPC). Supporting various federated learning scenarios, FATE now provides a host of federated learning algorithms, including logistic regression, tree-based algorithms, deep learning and transfer learning. FATE became open-source in February 2019. FATE TSC was established to lead FATE open-source community, with members from major domestic cloud computing and financial service enterprises. ...

Key Transparency is a system for accountable public-key discovery that lets users and senders verify the keys associated with an account over time. It combines an append-only log with a verifiable map so changes to a user’s keys produce cryptographic proofs, enabling clients to detect malicious insertions or undetected key rotations. The architecture separates operators from verifiers: even if the service is compromised, independent clients can audit inclusion and consistency proofs to...

...The code demonstrates how to process frame-level features, train logistic and deep learning models, evaluate them using metrics like global Average Precision (gAP) and mean Average Precision (mAP), and export trained models for MediaPipe inference.

An open source, GitOps, zero-trust secrets encryption and decryption solution for Kubernetes applications. Kamus enables users to easily encrypt secrets that can be decrypted only by the application running on Kubernetes. The encryption is done using strong encryption providers (currently supported: Azure KeyVault, Google Cloud KMS, Amazon Web Services KMS, and AES).

An encryption standard based on the chaotic properties of the logistic map function. With a pseudo-random byte generator, this cipher offers encryption with great statistical improbability.

Here we test the Logistic Map using finite and infinite precision floats (Apfloats). More decimals produce longer sequence (longer chaotic signal for certain parameters). Shorter decimal precision makes the sequence converge to zero eventually. In the process, number of decimals rises and then drops for some reason, forming a bell-shaped curve with long tail.