Introduction to Secure Multi-party Computation (SMPC)

Learn how secure multi-party computation enables parties to jointly compute a function over inputs while preserving privacy, without a central authority, using secret sharing, homomorphic encryption, and oblivious transfer.

Illustrate secure multi-party computation with a hospital use case. Show how data is prepared, protocol designed, and encrypted computation preserves patient privacy while yielding a diagnosis.

Trace the origins of secure multi-party computation from Yao's 1982 protocol to Goldwasser and Micali's breakthroughs, including secret sharing, oblivious transfer, garbled circuits, and homomorphic encryption.

Learn how cryptography secures communication by turning plaintext into ciphertext with encryption algorithms and keys, compare symmetric and asymmetric cryptography, and cover confidentiality, integrity, authenticity, and non-repudiation.

Explore symmetric and asymmetric cryptography, including secret and public keys, block and stream ciphers (AES, DES, Arc4, ChaCha20), digital signatures, and tradeoffs in speed and key management.

Master secret sharing as the backbone of secure multi-party computation, distributing secrets into shares and enabling reconstruction only above a threshold through polynomial interpolation, preserving privacy, robustness, and scalability.

Discover garbled circuits, a construction to evaluate functions privately by garbling wires and gates. The protocol uses garbled tables and oblivious transfer to ensure privacy, correctness, and resistance to attacks.

Learn oblivious transfer, a secure multi-party computation primitive where a sender reveals the chosen message, enabling one-out-of-two and k-out-of-n protocols and applications like private set intersection and private information retrieval.

Explore homomorphic encryption and zero knowledge proofs, enabling computations on encrypted data in secure multi-party computation while preserving privacy, acknowledging overhead and reliance on mathematical assumptions.

Explore zero-knowledge proofs, cryptographic protocols that let a prover convince a verifier of a statement's validity without revealing data, enabling secure multi-party computations (smpc).

Explore secure multi-party operations, including submission, comparison, and function evaluation, as building blocks of secure multi-party computation, using secret sharing, homomorphic encryption, and zero-knowledge proofs to enable private data collaboration.

Compute the sum of private inputs without revealing them, using secure multiparty summation. Prepare inputs, encode with secret sharing or homomorphic techniques, perform addition, and obtain the final confidential sum.

Learn secure multi-party comparison, a cryptographic protocol enabling multiple parties to compare private inputs to reveal only order or equality, using secret sharing, oblivious transfer, or garbled circuits.

Learn how secure function evaluation enables multiple parties to compute a function on private inputs using secret sharing, homomorphic encryption, or garbled circuits, while preserving privacy and result integrity.

Explore secure set intersection (ssi), a privacy-preserving multi-party protocol that reveals only the intersection of private sets using oblivious transfer, homomorphic encryption, or secure comparison within secure multi-party computation.

Learn how secure matrix multiplication enables parties to jointly compute products of private matrices while preserving privacy with secret sharing or homomorphic encryption.

Explore secure machine learning algorithms that enable collaborative training and inference on private data using cryptographic protocols like secure aggregation, federated learning, and encrypted inference, while preserving data privacy.

Explore privacy preserving data analytics with Smpc, enabling secure aggregation of data to compute mean, variance, and median, while enabling collaborative classification and differential privacy enhancements.

Discover how secure multi-party computation enables collaborative machine learning and model training across distributed data through federated learning and privacy preserving techniques.

Explore how secure multi-party computation enables secure auctions by preserving bidder privacy and ensuring integrity, using homomorphic encryption for bit confidentiality and differential privacy for anonymity.

Private information retrieval leverages SMPC protocols to securely query distributed databases, using oblivious transfer for privacy and differential privacy to protect user anonymity.

Discover how SMPC enables secure genomic analysis and healthcare privacy while supporting secure, verifiable electronic voting systems that protect voter privacy.

Explore smpc implementation tools, from python libraries to commercial and open frameworks that use secret sharing and homomorphic techniques for secure multi-party computations.

Celebrate the completion of this course and invite learners to connect on Udemy or any of my social media pages. Thank learners for their time and wish them well.