Abstract
Secure multi-party computation enables a group of parties to compute a function while jointly keeping their private inputs secret. The term “secure” indicates the latter property where the private inputs used for computation are kept secret from all other parties. A significant benefit of using secure multi-party computation is that many constructed protocols are information-theoretically secure, avoiding many problems using cryptographic hardness assumptions. Some notable use cases are secure auctions, privacy-preserving network security monitoring, spam filtering on encrypted emails, and secure machine learning. Secure multi-party computation can be used to secure and enable privacy-preserving applications from privacy-preserving network security to secure machine learning.
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1 Introduction
Secure Multi-Party Computation enables a group of parties to compute a function while jointly keeping their private inputs secret. The chapter discusses the definition of secure multi-party computation, its benefits and drawbacks, and its potential applications. It also discuss the trends in the field until 2025 and the challenges that need to be addressed for widespread adoption. Finally, the implementation possibilities for secure multi-party computation in Switzerland and the different deployment variations are discussed. The author provides recommendations for different markets and the need to consider deployment options.
2 Analysis
2.1 Definition
Secure Multi-Party Computation (MPC) enables a group of m mutually distrusting parties to jointly compute the outputs of a function \(f(x_1, x_2, ..., x_m)\) where \(x_i\) is the ith party’s private inputs without disclosing their private inputs [1]. The term “secure” indicates the latter property where the private inputs used for computation are kept secret from all other parties. Some MPC protocols allow for auditable computation allowing any party, including a party who did not participate in the computation, to verify the correctness of the result [2, 3].
A significant benefit of using MPC is that many of the constructed MPC protocols are information-theoretically secure, avoiding many of the problems involved with using cryptographic hardness assumptions. However, using MPC comes at the cost of performance (several orders of magnitudes slower), primarily due to MPC’s high bandwidth requirements. Nonetheless, specialized MPC protocols can significantly enhance performance compared to generic MPC protocols; one prominent example is private set intersection [4]. A drawback of information-theoretic MPC protocols in comparison to MPC protocols that rely on hardness assumptions that their security guarantees are violated in the presence of a dishonest majority [5].
One particular case of multi-party computation is private set intersection (PSI). In this case, each party has a set of items, and the goal is to learn the intersection of those sets while revealing nothing else about those sets [6].
2.2 Trends
Virtually all organizations could see benefits from utilizing MPC as it enables mutually distrustful parties to cooperatively compute the output of a function that they all agree on without revealing their input. These parties may be distinct. (e.g., different healthcare providers aiming to collaborate to improve patient care but do not want to disclose patient data) or the same (e.g., an organization aiming to protect sensitive information by splitting this information across its multiple data centers, where each data center is a party to the MPC protocol).
Some notable MPC use cases are secure auctions [7], privacy-preserving network security monitoring [8], spam filtering on encrypted emails [9] and secure machine learning [10]. Another notable MPC application is distributed authentication where MPC can strengthen an organization’s key server by splitting the critical server’s functionalities across multiple servers; an adversary capable of compromising one or a threshold of critical servers will not be able to reconstruct the organizations’ keys. Please refer to Chap. 13 for additional information on multi-party threshold systems. Unfortunately, factors such as a steep learning curve, unfamiliar mathematical notions, and a rapidly growing and evolving environment prevent easy exploitation of the technology by programmers and end users. To reach a widespread adoption of MPC, these issues must be addressed [11]. Application Programming interfaces (APIs) for secure multiparty computations are a promising technology to overcome these challenges. Another one are compilers.
3 Consequences for Switzerland
3.1 Implementation Possibilities: Make or Buy
An MPC solution consists of two major disciplines (distributed systems & cryptography), each with its challenges and it would thus require extensive efforts to design and implement a homemade MPC solution. The author then recommends purchasing an existing MPC solution for all markets (military, civil society, and economy) Nevertheless, he recommends different deployments as discussed in Sect. 17.3.2.
3.2 Variations and Recommendation
There are three MPC deployment variations: on-premise, hybrid, and cloud. For the military and maybe for civil society, the preferable setup is on-premise to prevent distributing private inputs to the software provider. To achieve the promises of MPC, an on-premise setup should require two or more independent data centers where each data center is considered a party to the MPC protocol. For civil society and the economy, the likely preferable option is a hybrid setup where the client’s IT infrastructure and the software provider’s IT infrastructure are each a party to the MPC protocol. The bandwidth between these two parties could be significant but may save the client from compartmentalizing their IT infrastructure. Cloud deployment allows for the complete outsourcing of the MPC solution where it is operated only on the software provider’s IT infrastructure. This cloud deployment is likely the least expensive option.
4 Conclusion
In conclusion, MPC enables a group of mutually distrusting parties to compute an agreed-upon function using their own private inputs without revealing their private inputs to other parties. MPC can be used to secure and enable privacy-preserving applications from privacy-preserving network security to secure machine learning. Given the complexity of designing and implementing MPC protocols, enlisting an MPC provider is preferable, but clients should have flexibility over the type of MPC deployment: on-premise, hybrid, and cloud.
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Merino, LH., Cabrero-Holgueras, J. (2023). Secure Multi-Party Computation. In: Mulder, V., Mermoud, A., Lenders, V., Tellenbach, B. (eds) Trends in Data Protection and Encryption Technologies . Springer, Cham. https://doi.org/10.1007/978-3-031-33386-6_17
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