Secure multi-party computation (SMPC) has emerged as a revolutionary technology for unlocking the collaborative potential of data while retaining confidentiality assurances based on advanced cryptography.
In this extensive guide, we discuss:
- What is secure multi-party computation and how it enables privacy-preserving collaborative analytics
- Example applications across industries and benefits realized
- Technical foundations, properties, latest innovations and future outlook
- Implementation considerations and vendor landscape
What is Secure Multi-Party Computation?
Secure multi-party computation (SMPC) refers to a collection of cryptographic protocols that allow multiple entities to jointly compute a function over their inputs while keeping the individual inputs private.
For example, SMPC allows banks to collectively detect fraudulent transactions by analyzing aggregated customer data without revealing personally identifiable information outside each bank‘s firewall.
At a high level, SMPC works by distributing data into encrypted fragments which can be mathematically operated on but not decoded. The results of the computation similarly remain scrambled and only the final output is revealed.
Source: An Analysis of Privacy-Preserving Data Mining
Let‘s analyze the key capabilities enabled by SMPC protocols:
Collaborative Analytics
SMPC allows deriving shared insights between multiple entities while keeping sensitive data inputs decentralized and private. For instance, fraud detection across financial institutions, risk scoring across insurance providers, demand forecasting across retailers etc.
Such cross-organization analytics expands the amount of data available for building ML models without centralizing data. This leads to superior and more robust models.
Unlocking New Use Cases
By taking compliance and competitiveness limitations around data sharing off the table, SMPC opens doors for analytics use cases not possible otherwise:
- Threat intelligence sharing across security vendors
- Precision medicine through aggregated genomic research data
- Safeguarding trade secrets when outsourcing computations to the cloud
Maintaining Confidentiality Assurances
With SMPC, raw data does not move outside trusted boundaries as is required today for analytics. By cryptographically computing on encrypted data, confidentiality and privacy assurances remain intact throughout.
This allows deriving insights from highly sensitive datasets like medical records, classified documents or confidential business data.
Thus SMPC squarely takes on the tradeoff between utility and privacy – enabling utility derived from data collaboration while providing strong privacy guarantees.
Real-World Applications and Benefits
The unique capabilities unlocked by SMPC around collaborative analytics on sensitive data are leading to adoption across a diverse set of industries:
Fraud Prevention in Finance
Banks can securely share and analyze financial transaction data related to suspected fraudulent accounts, loan applicants, wire transfers etc. without exposing customer data.
One consortium of over 15+ banks uses MPC for fraud analytics and has achieved $7 million in cost savings over 5 years related to fraud losses based on more accurate detections.
Medical Research
Healthcare providers can share sensitive patient health data like genomes, lab tests results, treatment history etc. to conduct aggregated analysis for research studies.
In one instance, SMPC analysis of data from multiple cancer patients‘ DNA identified genetic indicators across tumor types not detectable with individual data.
Securing Connected Vehicles
Telematics data gathered from vehicle sensors can be collaboratively analyzed to detect potential cybersecurity threats without compromising privacy.
One proof-of-concept detected GPS spoofing attacks from analyzing distributed data across just 15 vehicles with 96% accuracy.
The above examples highlight the unprecedented analytical capabilities being unlocked across domains while maintaining critical privacy assurances.
According to industry surveys, over 60% of IT decision makers expect SMPC adoption to accelerate given its ability to address data privacy and compliance requirements.
Technical Foundations of Secure Multi-Party Computation
Under the hood, SMPC involves innovating on areas of cryptography like homomorphic encryption, secret sharing, garbled circuits and zero-knowledge proofs.
Let‘s analyze some of the key technical mechanisms that enable privacy-preserving distributed computation:
Homomorphic Encryption
Homomorphic encryption schemes allow certain arithmetic operations to be performed directly on encrypted data without requiring decryption first.
For instance, multiplying two encrypted operands E(x) and E(y) yields the encrypted product E(x y).*
Thus homomorphic encryption permits secure mathematical computations while retaining confidentiality. However, only a subset of operations are supported. Fully homomorphic encryption remains prohibitively expensive for many applications.
Secure Multi-Party Computation (MPC) Protocols
MPC protocols enable arbitrary computations on distributed private data among multiple participants based on cryptographic techniques like linear secret sharing, garbled circuits, oblivious transfer etc.
In MPC, sensitive data is mathematically split into random shares using techniques like Shamir‘s Secret Sharing such that:
- Shares appear encrypted and reveal nothing about original data
- Combining a subset of shares reconstructs the secrets
Source: Deloitte
Computations occur on these shares through iterative secure multi-party protocols.
For example, calculating an average across values:
- Each party secretly shares its value by splitting into random shares
- Shares are summed across parties by operating directly on encrypted shares
- Final shares reveal averaged aggregate value only
Up to a threshold number of colluding/compromised participants can be tolerated without secret leakage.
Combining MPC protocols with trusted execution environments that protect against side-channel attacks during computation yields efficient and highly secure SMPC implementations.
Recent advances have yielded protocols for complex ML models like logistic regression, neural networks, random forest etc. with reasonable overheads.
Secure Multi-Party Computation vs. Related Privacy Technologies
SMPC occupies a critical spot among the landscape of privacy-enhancing technologies:
| Technology | Privacy Mechanism | Utility | Computation Complexity |
|-|
| Secure multi-party computation (SMPC) | Distributed encrypted data fragments
Cryptographic secure protocols | Sensitive data analysis, sharing
ML model building | Medium – High|
| Differential privacy | Inject statistical noise | Query response
Statistical analytics | Low |
| Federated learning| Decentralized model training | Shared ML model creation | Medium |
| Synthetic data | Fake data mirroring actuals | Algorithm development
analytics | Low-Medium |
While differential privacy and synthetic data provide lower utility, they involve simpler computations. Federated learning has medium complexity but may have high communication costs.
Among alternatives, SMPC provides the highest utility in terms of computational capability on sensitive data while also ensuring privacy – making it complementary for high-value use cases.
Trusted Execution Environments
Trusted execution environments (TEEs) like Intel‘s SGX provide hardware-enforced isolated memory regions for code execution. This protects against unauthorized access or modification of applications handling sensitive data – even from high-privilege code.
By leveraging TEEs, computation intensive cryptographic operations involved in SMPC can be efficiently run with minimal risk of side-channel attacks.
Integration of TEEs with MPC protocols has yielded up to 8-10x performance gains as per benchmarks.
Implementation Considerations
While conceptually transformational, practically deploying SMPC involves navigating additional complexities:
Infrastructure Requirements
SMPC demands high compute density given intensive public-key cryptography workloads.
Hybrid architectures combining on-premise trusted hardware like HSMs with secure cloud infrastructure provide an ideal foundation.
Efficient networking is also critical for lowering communication overheads in multi-party computations.
Platform Capabilities
Easy integration with existing analytic workflows is imperative, necessitating seamless interoperability of SMPC software platforms:
- Flexible connectivity to utilize on-premise data sources
- Developer APIs, analytics SDKs
- Orchestration of workflows involving external systems
Support for efficient distributed credential management across participants is also essential Workflow transparency provides visibility into operations.
Evaluating SMPC Systems
Both qualitative and benchmarks are required for analyzing effectiveness:
Qualitative
- Standards conformance – understands guarantees
- Provider diligence for assurance
Quantitative
- Microbenchmarks – computational overhead
- End-to-end tests – overall efficiency
published benchmarks on representative industry workloads are growing.
Emerging Best Practices
Implementations augmenting existing analytics pipelines should focus on specific high-value use cases first prior to enterprise-wide adoption.
With emerging turnkey SMPC solutions, optimal strategy is to start with transparent deployments on incremental datasets. This allows measuring benefits around enhanced models while limiting risks.
The Vendor Landscape
Specialized cryptographic vendors offer core SMPC technologies and protocols that can be integrated into custom solutions:
Leading Protocol Providers
- Unbound Tech
- Partisia
- Sepior
End-to-end SMPC analytics platforms tailored for enterprise use are also available:
Platform Providers
- Inpher
- Privitar
- Galois
Multiple cloud providers plan to onboard SMPC services for customer workloads.
Recent valuations of some leading vendors have exceeded $1 billion highlighting surging market interest.
The Road Ahead
Ongoing research and advances promise to further enhance SMPC capabilities:
Lowering Overheads
- Efficient multi-party computation protocols
- Specialized hardware like FPGAs
Security
- Resisting side-channel attacks
- Higher collusion resistance
Ease-of-Use
- Automation for minimizing user errors
- Developer friendly abstractions
Applications
- Custom solutions for vertical use cases
- Integration with blockchain, Web3 systems
Gartner predicts that by 2025, over 40% of large organizations will use privacy-enhancing computation technologies like SMPC in analytics engineering, testing and pre-production phases.
The Societal Impact
Widespread adoption of SMPC has the potential to catalyze data sharing across organizational boundaries – unlocking new frontiers of innovation while retaining critical privacy rights.
Such mutually beneficial data collaboration can profoundly impact domains like healthcare, education and even smart cities – ultimately furthering public good.
As vectors for coordination and trust, SMPC systems will form a crucial substrate for emerging decentralized ecosystems, unlocking new human possibilities based on cooperation while eliminating unnecessary secrecy.