Secure multi-party computation is a cryptographic technique that allows different parties to jointly perform a computation on their private inputs without revealing them to one another or any external entities. This method guarantees data privacy in scenarios where sensitive information must be processed in a privacy-preserving manner.
MPC is used when organizations need to collaborate on data analysis without sharing raw sensitive information. It is widely applied in financial institutions, healthcare, and government agencies, where secure computation is necessary for privacy, data protection, and regulatory compliance.
This approach removes the need for a trusted third party, allowing businesses to work together while keeping their sensitive data confidential. Whether it’s fraud detection, medical research, or cybersecurity, MPC makes privacy-preserving computation possible without sacrificing security or control.
In an MPC protocol, two or more parties hold private inputs and want to compute a function based on them without exposing their inputs to each other. Instead of sharing raw data, each party contributes encrypted data fragments that, when combined, produce a valid output while preserving the privacy of their data.
For example, in healthcare research, multiple hospitals may need to analyze patient data for rare disease detection. With MPC-powered data collaboration, they can jointly compute patient records to identify health patterns without exposing personal health information (PHI). This enables insights into genomics, medical trials, and other rare health conditions while maintaining HIPAA compliance.
The use of multiparty computation extends to various industries, including:
The ability to compute functions over multiple private inputs without exposing sensitive data offers several advantages:
No Need for a Trusted Third Party: SMPC removes reliance on a central authority to manage data exchanges. Each participant keeps full control over their private data, reducing vulnerabilities and limiting exposure to insider threats or external breaches.
Privacy Preservation: Data remains confidential throughout computations, allowing multiple parties to collaborate without revealing their inputs. This approach supports industries where data sensitivity is a concern, such as healthcare and finance.
Improved Security: Instead of centralizing data, SMPC distributes it across multiple entities using secret sharing, garbled circuits, and oblivious transfer. These cryptographic protocols reduce the risk of breaches by ensuring no single party has access to the full dataset.
Regulatory Compliance: SMPC supports compliance with data protection laws, including GDPR, HIPAA, and CCPA by keeping data within its original location and avoiding unauthorized transfers. Organizations operating across multiple jurisdictions benefit from this approach.
Accurate and Reliable Results: Unlike data anonymization or differential privacy methods that may introduce noise to protect sensitive information, SMPC performs exact computations on full datasets without data masking. This allows for precise and reliable results while preserving privacy.
Quantum-Resistant Protection: SMPC uses cryptographic techniques that safeguard computations against future quantum computing threats, ensuring long-term security. This is also known as post-quantum security.
“SMPC can be enhanced with quantum-resistant cryptographic techniques to safeguard computations against future quantum threats.”
While SMPC offers strong privacy-preserving benefits, it also has limitations that impact its practical application:
Computational Overhead: Random number generation, encryption, and interactive cryptographic operations are necessary to maintain security during computation. However, these processes introduce additional computational overhead, which can slow down execution time and make SMPC less efficient than traditional plaintext computing.
Latency Issues: SMPC computations require different parties to exchange information in a privacy-preserving manner, leading to higher latency. This delay can be problematic in real-world scenarios where quick decision-making is essential.
Threshold Limitations: Some secure protocols have a threshold limit on the number of participants must be present for computations to proceed. As more entities engage in privacy-preserving computation, the process becomes more complex, and the risk of malicious adversaries or attacks increases.
High Communication Costs: Secret sharing and linear secret sharing require continuous communication between parties. This increases communication costs, making SMPC less scalable for large-scale applications. Optimizations like batching and parallelization can help, but scalability remains a challenge.
Risk of Collusion: In multi-party computation, collusion between a subset of participants can compromise privacy if the protocol is not designed to withstand such threats. To mitigate this risk, SMPC protocols implement cryptographic safeguards like threshold cryptography, zero-knowledge proofs, and privacy zones that enforce data protection across separate domains.
Secure Multi-Party Computation (SMPC) offers significant benefits in privacy, security, and trustless collaboration, allowing multiple parties to compute on sensitive data without revealing their inputs. However, implementing SMPC can be complex due to computational overhead, communication costs, and latency.
Duality Technologies provides advanced privacy-preserving computation solutions, enabling organizations to analyze sensitive data securely. By integrating secure multiparty computation (SMPC), homomorphic encryption, secret sharing, and zero-knowledge proofs, Duality ensures compliance with data protection regulations while maintaining high performance.
Contact us to learn how our solutions can help safeguard your data.
