6 Repos
Utilities and architectural patterns designed to protect sensitive information through local processing and offline operation.
Distinguishing note: Focuses on privacy-preserving local-first security measures rather than network-level encryption or authentication.
Explore 6 awesome GitHub repositories matching security & cryptography · Data Privacy Tools. Refine with filters or upvote what's useful.
Cherry Studio is a cross-platform desktop application that serves as a centralized workspace for managing and interacting with multiple artificial intelligence models. It functions as a local-first orchestrator, prioritizing user privacy by storing all conversation history and knowledge bases directly on your device. By providing a unified interface for both cloud-based and local AI services, the platform simplifies API key management and allows for consistent model interaction across different operating systems. The application distinguishes itself through a robust retrieval-augmented genera
Protect sensitive information by operating locally, performing manual backups, and using synchronization services to ensure your data remains private and available across your devices.
GKD is an automation engine that leverages the Android accessibility framework to monitor system events and execute predefined interaction rules. By acting as a background service, it identifies interface elements in real-time and performs automated tasks, such as dismissing pop-ups or navigating through application screens, based on the current state of the device. The project distinguishes itself through a declarative rule engine that processes human-readable configuration files. Users can define sophisticated interaction logic using a specialized selector syntax that supports hierarchical
Edit captured screenshots or modify node data to remove personal information before sharing files with others to ensure privacy and data security.
This project is a local-first task manager and time tracking tool designed to consolidate work items from multiple external project management platforms into a single, unified interface. By prioritizing local data sovereignty, it ensures that all task lists, time logs, and application states remain on the user's device, providing full functionality in offline environments while maintaining privacy. The application distinguishes itself through a focus on deep work and structured productivity rituals. It integrates distraction-free modes, configurable focus timers, and automated time tracking t
Operates entirely on the local device to ensure data remains under user control and private.
Planning with files is an enterprise knowledge graph platform designed to transform unstructured organizational data into a searchable, interconnected network. By utilizing a graph-based retrieval-augmented generation engine, the system grounds language model outputs in verified internal data, ensuring that responses are explainable, traceable, and free from hallucinations. The platform distinguishes itself through a focus on data sovereignty and secure, private infrastructure deployment. It enables organizations to maintain full control over sensitive information by processing data locally o
Enforces data privacy through local processing and secure architectural patterns that keep information under user control.
This project is a command-line utility designed to monitor and analyze token consumption and financial expenditure for AI coding assistants. By parsing local session logs directly on the user's machine, it provides a privacy-focused way to track development activity without transmitting sensitive data to external servers. The tool distinguishes itself through its ability to aggregate disparate log formats from multiple coding assistants into a unified, schema-agnostic representation. It features a decoupled pricing engine that allows users to apply custom model-specific cost multipliers, over
Performs all analysis locally on the user machine without transmitting usage data to external servers.
SecretFlow is a privacy computing framework and platform designed for secure multi-party computation, federated learning, and privacy-preserving data analysis across independent nodes. It provides a management system to coordinate secure workloads and cryptographic tasks across a distributed cluster. The project enables joint data analysis and machine learning on partitioned datasets using cryptographic protocols. It allows for the training of models and the execution of analytical queries across multiple parties without exposing raw source information to any single participant. The framewor
Performs analytical queries and data processing across distrusting parties using secure computation.