Search Results for "differential privacy"
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Showing 12 open source projects for "differential privacy"
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Gemini 3 and 200+ AI Models on One PlatformBuild generative AI apps with Vertex AI. Switch between models without switching platforms.
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MongoDB Atlas runs apps anywhereMongoDB Atlas gives you the freedom to build and run modern applications anywhere—across AWS, Azure, and Google Cloud. With global availability in over 115 regions, Atlas lets you deploy close to your users, meet compliance needs, and scale with confidence across any geography.
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Opacus
Training PyTorch models with differential privacy
...Open source, modular API for differential privacy research. Everyone is welcome to contribute. ML practitioners will find this to be a gentle introduction to training a model with differential privacy as it requires minimal code changes. Differential Privacy researchers will find this easy to experiment and tinker with, allowing them to focus on what matters. -
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TensorFlow Privacy
Library for training machine learning models with privacy for data
Library for training machine learning models with privacy for training data. This repository contains the source code for TensorFlow Privacy, a Python library that includes implementations of TensorFlow optimizers for training machine learning models with differential privacy. The library comes with tutorials and analysis tools for computing the privacy guarantees provided. -
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Appfl
Advanced Privacy-Preserving Federated Learning framework
APPFL (Advanced Privacy-Preserving Federated Learning) is a Python framework enabling researchers to easily build and benchmark privacy-aware federated learning solutions. It supports flexible algorithm development, differential privacy, secure communications, and runs efficiently on HPC and multi-GPU setups. -
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Outlook Google Calendar Sync
Sync your Outlook and Google calendars
...Supports all versions of Outlook from 2003 to 2019/Microsoft365 64-bit! Installable and portable options - even runs from a USB thumbdrive. Synchronises items in any calendar folder, including those shared with you. Differential comparison updates only attributes that have changed. Customizable date range to synchronize, past and future. Frequency of automatic syncs, including push-sync from Outlook. Configurable proxy settings, or use Internet Explorer's. Merge new events into existing on the destination calendar. Prompt on deletion of items. Ability to obfuscate custom words for privacy/security. ... -
Powerful App Monitoring Without Surprise BillsTired of monitoring tools that punish you for scaling? AppSignal offers transparent, predictable pricing with every feature unlocked on every plan. Track errors, monitor performance, detect anomalies, and manage logs across Ruby, Python, Node.js, and more. Trusted by developers since 2012 with free dev-to-dev support. No credit card required to start your 30-day trial.
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NVIDIA FLARE
NVIDIA Federated Learning Application Runtime Environment
NVIDIA Federated Learning Application Runtime Environment NVIDIA FLARE is a domain-agnostic, open-source, extensible SDK that allows researchers and data scientists to adapt existing ML/DL workflows(PyTorch, TensorFlow, Scikit-learn, XGBoost etc.) to a federated paradigm. It enables platform developers to build a secure, privacy-preserving offering for a distributed multi-party collaboration. NVIDIA FLARE is built on a componentized architecture that allows you to take federated... -
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Xfl
An Efficient and Easy-to-use Federated Learning Framework
XFL is a lightweight, high-performance federated learning framework supporting both horizontal and vertical FL. It integrates homomorphic encryption, DP, secure MPC, and optimizes network resilience. Compatible with major ML libraries and deployable via Docker or Conda. -
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BlackBelt WASTE - ipv4/Tor/i2p +AI+Voice
Modern, AI-Smart, WASTE p2p for ipv4, Tor and i2p + Voice Conference.
Open Source - GPLv3 inc images. A WASTE client. Download and create your own WASTE networks. Move 1000's of GB's at 100MB+ per sec. (800 Mbits per sec) FULL pause and resume capable. Voice Conference, Chat, Transfer files and Participate in Forums in a secure environment. For Windows XP 32/64, Vista 32/64, Win7 32/64, Win8 32/64, Win 10, Win 11, Linux (WINE). *** User Based Access Control - for voice, chats, file transfers and uploads. (useful in NULLNETS) *** Distributed... -
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Functional Mechanism
Regression Analysis under Differential Privacy
Functional Mechanism is a differentially private method designed for a large class of optimization-based analyses. The main idea is to enforce epsilon-differential privacy by perturbing the objective function of the optimization problem, rather than its results. Please cite the following paper if you choose to use this code: J. Zhang, Z. Zhang, X. Xiao, Y. Yang, and M. Winslett. Functional Mechanism: Regression Analysis under Differential Privacy. PVLDB 5(11):1364-1375, 2012. -
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PrivLocal/PrivGene
Differentially Private Model Fitting Using Genetic Algorithms
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Stop Storing Third-Party Tokens in Your DatabaseRolling your own OAuth token storage can be a security liability. Token Vault securely stores access and refresh tokens from federated providers and handles exchange and renewal automatically. Connected accounts, refresh exchange, and privileged worker flows included.
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This is the code used in the experiments of the following paper: Xiaokui Xiao, Guozhang Wang, Johannes Gehrke: Differential Privacy via Wavelet Transforms. IEEE Trans. Knowl. Data Eng. 23(8): 1200-1214 (2011). Please cite the paper if you choose to use the code.
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DiffGen
Differentially-private algorithm based on Generalization
Privacy-preserving data publishing addresses the problem of disclosing sensitive data when mining for useful information. Among existing privacy models, epsilon-differential privacy provides one of the strongest privacy guarantees and has no assumptions about an adversary's background knowledge. All the existing solutions that ensure epsilon-differential privacy handle the problem of disclosing relational and set-valued data in a privacy preserving manner separately. ... -
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VaultGemma
VaultGemma: 1B DP-trained Gemma variant for private NLP tasks
VaultGemma is a sub-1B parameter variant of Google’s Gemma family that is pre-trained from scratch with Differential Privacy (DP), providing mathematically backed guarantees that its outputs do not reveal information about any single training example. Using DP-SGD with a privacy budget across a large English-language corpus (web documents, code, mathematics), it prioritizes privacy over raw utility. The model follows a Gemma-2–style architecture, outputs text from up to 1,024 input tokens, and is intended to be instruction-tuned for downstream language understanding and generation tasks. ...
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