Definition
Secure Multi-Party Computation (SMPC) is a group of cryptographic protocols that allow several parties to jointly compute a function over their inputs while keeping those inputs completely private. Each participant learns only its own input and the final output, but gains no additional information about other parties' data.
SMPC provides strong confidentiality guarantees even in adversarial scenarios and is a central component of privacy-preserving computation frameworks.
Core properties
- Input privacy - private data is never exposed to other participants.
- Correctness - the result matches that of computation performed on pooled plaintext data.
- Adversarial robustness - protocols may operate under semi-honest, malicious, or covert threat models.
- No trusted third party - computation is distributed mathematically among participants.
Threat models
- Semi-honest adversary - follows the protocol but attempts to infer additional information.
- Malicious adversary - may deviate from the protocol to manipulate or extract information.
- Covert adversary - attacks but avoids actions that would reveal misconduct.
Common SMPC techniques
- Secret Sharing - splitting data into multiple shares that individually reveal nothing.
- Beaver Triples - precomputed values enabling efficient secure multiplication.
- Yao’s Garbled Circuits - evaluation of encrypted boolean circuits.
- Additive Sharing - decomposition of values into additive random components.
- Threshold Cryptography - keys divided among participants requiring a quorum to reconstruct.
Operational workflow
- Each party splits its input into secret shares.
- Shares are exchanged between participants or computation nodes.
- Secure computation is performed on shares using protocol-specific rules.
- The output is reconstructed from the resulting shares.
Technical metrics
Metric | Explanation |
Communication Complexity | Total amount of data exchanged; major factor in scalability. |
Round Complexity | Number of communication rounds required to complete the protocol. |
Computational Overhead | Additional cryptographic operations compared to plaintext computation. |
Security Threshold | Number of compromised participants tolerated without losing confidentiality. |
Advantages
- Enables collaborative results without sharing private data.
- Supports GDPR principles (data minimization, purpose limitation).
- Eliminates the need for centralizing sensitive datasets.
- Resilient to data leaks - no single party ever holds full information.
Limitations
- High communication overhead for large-scale computations.
- Performance significantly slower than centralized processing.
- Implementation complexity and operational overhead.
- Some protocols require trusted setup.
Relevance to image and video anonymization
SMPC is highly valuable in distributed visual systems, especially when raw image or video data cannot be shared between organizations or devices. Relevant applications include:
- secure collaborative training of face or license plate detection models,
- joint event analysis without sharing raw footage,
- secure aggregation of metadata extracted from surveillance systems,
- privacy-preserving creation of anonymized datasets,
- secure coordination between edge devices and central servers.
In anonymization pipelines, SMPC enables safe exchange of intermediate representations (e.g., encrypted feature vectors or bounding box parameters) without exposing the underlying images.