A small repository to contain all the resources of Multi-Party Computation and Differential Privacy used by me to develop background for IBM Research collab project.
[Update] Note: This will not contain any of the research work in general; just all the literature-reading.

• Secure Multiparty Computation (MPC) by Yehuda Lindell (cryptographic protocols / survey): Paper | Video

• MP-SPDZ: A Versatile Framework for Multi-Party Computation by Marcel Keller : Paper

• SCALE-MAMBA Documentation & Guide : link

• Morten Dahl's blogs on MPC and SPDZ Protocol : link

• Bristol Cryptography Blog : link

• A Pragmatic Introduction to Secure Multi-Party Computation: PDF

• IBM's Differential Privacy library diffprivlib : installation guide | example notebooks | detailed documentation

• Google's Differential Privacy library Privacy on Beam : documentation | codelab | Github

• OpenMined's Python wrapper for Privacy on Beam PyDP : Github | Slack | excellent examples | resources & tutorials

• Facebook's high-speed library for training PyTorch models with differential privacy Opacus : Tutorial

• k-anonymity : Wikipedia

• OpenDP's SmartNoise SDK : GitHub

• Google's Differential Privacy library Algo's documentation (C++) : Docu

• TED's non-technical writing on Differential Privacy : Link

• Frankmcsherry's Blog : Link

• Uber's Differential Privacy Framework (based on Elastic Sensitivity) CHORUS: First Blog | Updated Blog | Video presentation | Elastic Sensitivity paper | GitHub

• Hierarchial Time Series: Tutorials

• Vector autoregression : Wikipedia | Tutorials | PennState Docu

• ARIMA model (Autoregressive Integrated moving averages : Wikipedia | Tutorials

• Working mechanisms of different DP libaries : Privacy on Beam | OpenDP

• Distributed DP mechanisms into MPC protocols: Paper 1 | Paper 2 | Paper 3

• Laplace mechanism for adding noise in ε-differential privacy: Paper 1 | Slides from Roger Grosse | Lecture of Gautam Kamath | Truncated Laplacian mechanism used by IBM

• Geometric mechanism used in IBM diffprivlib: Paper

• DLPA (Distributed Laplace Perturbation Algorithm) for optimally adding noise to attain Differential Privacy in a distributed manner when N users are participating in a computation: Paper (Rastogi and Nath)

• The parameters used in IBM diffprivlibfor calculating differentially private sum/mean: Reference Pg 41,44

• How to sample noise from Gaussian Distribution : 3 standard .py libs | numpy.random Docu

• MP-SPDZ for Secure MPC : Github | tutorial.mpc | mp-spdz documentation

• Different Functions(sum, mean, count, var, sd, quantiles) : IBM diffprivlib utils.py | IBM diffprivlib quantiles.py

• Meaning of Epsilon(ε) and Delta(δ) in (ε, δ)-differential privacy : Ref