Paaster is a secure by default end-to-end encrypted pastebin built with the objective of simplicity.

Overview

Follow the development of our desktop client here

Paaster

Paaster is a secure by default end-to-end encrypted pastebin built with the objective of simplicity.

Preview

Video of paaster in action! Mobile preview

Features

Looking to build a client for paaster?

Check out our Integration documentation

Security

What is E2EE?

E2EE or end to end encryption is a zero trust encryption methodology. When you paste code into paaster the code is encrypted locally with a secret generated on your browser. This secret is never shared with the server & only people you share the link with can view the paste.

Can I trust a instance of paaster not hosted by me?

No. Anyone could modify the functionality of paaster to expose your secret key to the server. We recommend using a instance you host or trust.

How are client secrets stored?

Client-sided secrets are stored in localStorage on paste creation (for paste history.) Anything else would be retrievable by the server or be overly complicated. This does make paaster vulnerable to malicious javascript being executed, but this would require malicious javascript to be present when the svelte application is built. If this was the case you'd have bigger issues, like the module just reading all inputs & getting the plain text paste.

How are client secrets transported?

Paaster uses URI fragments to transport secrets, according to the Mozilla foundation URI fragments aren't meant to be sent to the server. Bitwarden also has a article covering this usage here.

How are server secrets stored?

Server-sided secrets are stored in localStorage on paste creation, allowing you to modify or delete pastes later on. Server-sided secrets are generated on the server using the python secrets module & are stored in the database using bcrypt hashing.

Cipher

paaster is built using the forge module, using AES-256 in CBC mode with PKCS7 padding & PBKDF2 key derivation at 50,000 iterations. More details are located in our Integration documentation.

Shortcuts

  • Ctrl+V - Paste code.
  • Ctrl+S - Download code as file.
  • Ctrl+A - Copy all code to clipboard.
  • Ctrl+X - Copy URL to clipboard.

Requesting features

  • Open a new issue to request a feature (one issue per feature.)

What we won't add

  • Paste editing.
    • paaster isn't a text editor, it's a pastebin.
  • Paste button.
    • paaster isn't a text editor, when code is inputted it will always be automatically uploaded.
  • Optional encryption.
    • paaster will never have opt-in / opt-out encryption, encryption will always be present.

Setup

Production with Docker

  • git clone --branch Production https://github.com/WardPearce/paaster
  • Configure docker-compose.yml
  • Proxy exposed ports using Nginx (or whatever reverse proxy you prefer.)
  • FRONTEND_PROXIED should be the proxied address for "paaster_frontend". E.g. for paaster.io this is "https://paaster.io"
  • VITE_BACKEND should be the proxied address for "paaster_starlette". E.g. for paaster.io this is "https://api.paaster.io"
  • sudo docker-compose build; sudo docker-compose up -d

Using Rclone

Using rclone with Docker Compose

Basically the most important part is to install fuse, create /var/lib/docker-plugins/rclone/config & /var/lib/docker-plugins/rclone/cache, install the docker plugin docker plugin install rclone/docker-volume-rclone:amd64 args="-v" --alias rclone --grant-all-permissions, configure the rclone.conf for the storage service you want to use & then configure your docker compose to use the rclone volume. Example rclone docker compose.

Production without docker

This setup is not recommended & requires more research / knowledge.

  • git clone --branch Production https://github.com/WardPearce/paaster.
  • cd paaster-frontend
  • Create .env
    • VITE_NAME - The name displayed on the website.
    • VITE_BACKEND - The URL of the API.
  • Install nodejs
    • npm install
    • npm run build
  • Serve files generated in dist with Nginx (or whatever reverse proxy you use.)
  • cd paaster-backend
  • Install Python 3.7+
    • pip3 install -r requirements.txt
    • Configure main.py following the guide for uvicorn.
  • Pass environmental variables
    • REDIS_HOST
    • REDIS_PORT
    • MONGO_IP
    • MONGO_PORT
    • MONGO_DB
    • FRONTEND_PROXIED - The URL of the Frontend.
  • Proxy port with Nginx (or whatever reverse proxy you use.)

Development

  • git clone https://github.com/WardPearce/paaster.
  • cd paaster-frontend
  • Create .env
    • VITE_NAME - The name displayed on the website.
    • VITE_BACKEND - The URL of the API.
  • Install nodejs
    • npm install
    • npm run dev
  • cd paaster-backend
  • Pass environmental variables
    • REDIS_HOST
    • REDIS_PORT
    • MONGO_IP
    • MONGO_PORT
    • MONGO_DB
    • FRONTEND_PROXIED - The URL of the Frontend.
  • Install Python 3.7+
    • pip3 install -r requirements.txt
    • Run main.py
Owner
Ward
Privacy advocate & open source developer
Ward
Official Implementation for Encoding in Style: a StyleGAN Encoder for Image-to-Image Translation

Encoding in Style: a StyleGAN Encoder for Image-to-Image Translation We present a generic image-to-image translation framework, pixel2style2pixel (pSp

2.8k Dec 30, 2022
Location-Sensitive Visual Recognition with Cross-IOU Loss

The trained models are temporarily unavailable, but you can train the code using reasonable computational resource. Location-Sensitive Visual Recognit

Kaiwen Duan 146 Dec 25, 2022
This is a repository for a No-Code object detection inference API using the OpenVINO. It's supported on both Windows and Linux Operating systems.

OpenVINO Inference API This is a repository for an object detection inference API using the OpenVINO. It's supported on both Windows and Linux Operati

BMW TechOffice MUNICH 68 Nov 24, 2022
Recall Loss for Semantic Segmentation (This repo implements the paper: Recall Loss for Semantic Segmentation)

Recall Loss for Semantic Segmentation (This repo implements the paper: Recall Loss for Semantic Segmentation) Download Synthia dataset The model uses

32 Sep 21, 2022
Official PyTorch Implementation of Mask-aware IoU and maYOLACT Detector [BMVC2021]

The official implementation of Mask-aware IoU and maYOLACT detector. Our implementation is based on mmdetection. Mask-aware IoU for Anchor Assignment

Kemal Oksuz 46 Sep 29, 2022
Video Corpus Moment Retrieval with Contrastive Learning (SIGIR 2021)

Video Corpus Moment Retrieval with Contrastive Learning PyTorch implementation for the paper "Video Corpus Moment Retrieval with Contrastive Learning"

ZHANG HAO 42 Dec 29, 2022
Optimising chemical reactions using machine learning

Summit Summit is a set of tools for optimising chemical processes. We’ve started by targeting reactions. What is Summit? Currently, reaction optimisat

Sustainable Reaction Engineering Group 75 Dec 14, 2022
PyTorch implementation of MoCo v3 for self-supervised ResNet and ViT.

MoCo v3 for Self-supervised ResNet and ViT Introduction This is a PyTorch implementation of MoCo v3 for self-supervised ResNet and ViT. The original M

Facebook Research 887 Jan 08, 2023
[CVPR2021] Invertible Image Signal Processing

Invertible Image Signal Processing This repository includes official codes for "Invertible Image Signal Processing (CVPR2021)". Figure: Our framework

Yazhou XING 281 Dec 31, 2022
An self sufficient AI that crawls the web to learn how to generate art from keywords

Roxx-IO - The Smart Artist AI! TO DO / IDEAS Implement Web-Scraping Functionality Figure out a less annoying (and an off button for it) text to speech

Tatz 5 Mar 21, 2022
[NeurIPS 2021 Spotlight] Code for Learning to Compose Visual Relations

Learning to Compose Visual Relations This is the pytorch codebase for the NeurIPS 2021 Spotlight paper Learning to Compose Visual Relations. Demo Imag

Nan Liu 88 Jan 04, 2023
Image Captioning on google cloud platform based on iot

Image-Captioning-on-google-cloud-platform-based-on-iot - Image Captioning on google cloud platform based on iot

Shweta_kumawat 1 Jan 20, 2022
An implementation of the efficient attention module.

Efficient Attention An implementation of the efficient attention module. Description Efficient attention is an attention mechanism that substantially

Shen Zhuoran 194 Dec 15, 2022
FADNet++: Real-Time and Accurate Disparity Estimation with Configurable Networks

FADNet++: Real-Time and Accurate Disparity Estimation with Configurable Networks

HKBU High Performance Machine Learning Lab 6 Nov 18, 2022
A resource for learning about ML, DL, PyTorch and TensorFlow. Feedback always appreciated :)

A resource for learning about ML, DL, PyTorch and TensorFlow. Feedback always appreciated :)

Aladdin Persson 4.7k Jan 08, 2023
Time Series Forecasting with Temporal Fusion Transformer in Pytorch

Forecasting with the Temporal Fusion Transformer Multi-horizon forecasting often contains a complex mix of inputs – including static (i.e. time-invari

Nicolás Fornasari 6 Jan 24, 2022
Music Classification: Beyond Supervised Learning, Towards Real-world Applications

Music Classification: Beyond Supervised Learning, Towards Real-world Applications

104 Dec 15, 2022
Code for training and evaluation of the model from "Language Generation with Recurrent Generative Adversarial Networks without Pre-training"

Language Generation with Recurrent Generative Adversarial Networks without Pre-training Code for training and evaluation of the model from "Language G

Amir Bar 253 Sep 14, 2022
Syllabus del curso IIC2115 - Programación como Herramienta para la Ingeniería 2022/I

IIC2115 - Programación como Herramienta para la Ingeniería Videos y tutoriales Tutorial CMD Tutorial Instalación Python y Jupyter Tutorial de git-GitH

21 Nov 09, 2022
My coursework for Machine Learning (2021 Spring) at National Taiwan University (NTU)

Machine Learning 2021 Machine Learning (NTU EE 5184, Spring 2021) Instructor: Hung-yi Lee Course Website : (https://speech.ee.ntu.edu.tw/~hylee/ml/202

100 Dec 26, 2022