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TEEs and other cryptographic primitives

TEEZKMPCFHE
Core ConceptHardware-based secure enclave within a processor that isolates and protects data and code during execution, even from the host systemEnables one party to prove that a computation was performed correctly without revealing any additional informationAllows multiple parties to collaboratively compute a function over their inputs while keeping those inputs private from each other.Allows computation directly on encrypted data without needing to decrypt it
Trust AssumptionsInclude the integrity of the hardware and software components, the secure boot process that ensures only authorized code runs, and the isolation mechanisms that prevent unauthorized access to sensitive data and operations within the TEEInclude the hardness of certain mathematical problems (e.g., integer factorization), correctness of proof systems, trusted setups (if applicable)Rely on the parties following the protocol correctly, the presence of a threshold number of honest participants, and the cryptographic security of the underlying primitives against adversaries within specified computational or collusion bounds.include the belief in the mathematical soundness of the underlying cryptographic algorithms, the security of the key management processes, and the integrity of the computational environment where the encrypted data is processed, ensuring that no information is leaked during computation
Use CasesSecure key management, confidential data processing, secure machine learning, blockchain scalability, etc.Privacy-preserving authentication, secure blockchain transactions, regulatory compliance without data exposure, verifiable computation, etc.MPC allows multiple parties to compute functions over their private inputs without revealing those inputs to each other, and is particularly suitable for privacy-preserving collaborative data analysis and secure collaborative computationFHE enables computation on encrypted datasets without decryption and is suitable for secure and privacy-preserving data analytics, secure data processing
Limitation(s)Manufacturer dependency

Vulnerable to physical and side-channel attacks
High computational cost

Challenges in achieving efficiency and scalability for large-scale applications
High communication overhead

Complexity scales with number of participants
High computational overhead

Slow performance

Challenges in achieving practical efficiency for large-scale or real-time computations on encrypted data