How can you use secure multi-party computation in Distributed System Security?

Secure multi-party computation (SMPC) is a cryptographic technique that allows multiple parties to jointly compute a function on their private inputs, without revealing them to each other. SMPC can enable various applications that require privacy and security in distributed systems, such as data mining, machine learning, voting, auctions, and blockchain. In this article, you will learn how SMPC works, what are its benefits and challenges, and how you can use it in distributed system security.

1 SMPC Basics

SMPC is based on the idea of secret sharing, which is a method of splitting a secret into multiple pieces, called shares, such that only a certain number of shares can reconstruct the secret. For example, if Alice wants to share a secret number x with Bob and Charlie, she can use a polynomial function f(x) of degree 1, such that f(0) = x, and give f(1) to Bob and f(2) to Charlie. Then, Bob and Charlie can compute x by interpolating f(x) from their shares. However, if either of them loses or leaks their share, the secret is still safe. SMPC extends this idea to allow multiple parties to perform arithmetic operations on their shares, without revealing their inputs or intermediate results. For example, if Alice, Bob, and Charlie want to compute the average of their salaries, they can each secret share their salary with the others, and then add and divide their shares to obtain the average, without knowing each other's salary.

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Cleber Brito Alves

Analista Cloud e Inteligência Artificial | Certificações Microsoft (AI900, PL900, AZ900) e Ethical Hacking (EHE) | Desenvolvimento Low Code, Automação e Gestão de Projetos | Bacharel em TI
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A computação segura de várias partes em sistemas distribuídos se baseia em criptografia, autenticação, assinaturas digitais, MPC, cadeias de confiança e controle de acesso para garantir que diferentes partes possam colaborar de forma segura, protegendo dados e garantindo a integridade do sistema.

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Vanderlei de Jesus Barcala

LinkedIn Top Voice | Especialista em Clouds, Cibersegurança, Dados, DevOps, Serviços, Gerenciamento, ajudo empresas a impulsionar seus negócios digitalmente, tornando cada vez mais estratégicas.
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A SMPC permite a funcionalidade de “caixa negra”, onde diversos usuários podem trabalhar em um cálculo juntos usando informações privadas. Mesmo que todos possam ver o resultado, os dados pessoais são mantidos em segredo.

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Ariel Riera

Director de proyecto en Clarín | IT Project Implementation
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La informática multipartita segura (SMPC) en sistemas distribuidos se sustenta con fundamentos criptográficos y protocolos avanzados. La adopción de técnicas de cifrado robusto, autenticación rigurosa y firmas digitales, técnicas de Computación Segura entre Múltiples Partes (MPC) cadenas de confianza y la aplicación de mecanismos de control de acceso conforma un entramado integral que viabiliza la colaboración segura entre las entidades involucradas. Este enfoque garantiza la confidencialidad de la información, valida la autenticidad de los actores participantes, asegura la integridad de los datos intercambiados La aplicación de estos elementos constituye un marco integral y altamente seguro para el desarrollo de interacciones colaborativas

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2 SMPC Benefits

Secure Multi-Party Computation (SMPC) has several advantages for distributed system security, such as preserving the confidentiality of the parties' inputs and outputs, ensuring the accuracy of the computation, and reducing communication and computation overhead. SMPC can protect sensitive data from unauthorized access, leakage, or tampering, while preventing malicious parties from manipulating or falsifying the data or the function. Additionally, it can improve the scalability and availability of the system by allowing the parties to perform the computation locally on their shares, rather than sending their data to a central server or a third party.

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Vanderlei de Jesus Barcala

LinkedIn Top Voice | Especialista em Clouds, Cibersegurança, Dados, DevOps, Serviços, Gerenciamento, ajudo empresas a impulsionar seus negócios digitalmente, tornando cada vez mais estratégicas.
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A SMPC permite a computação em conjunto de diversos usuários, mantendo a confidencialidade dos inputs individuais e produzindo um output correto. É usada criptografia e o compartilhamento secreto (Secret Sharing) para garantir a privacidade e a segurança durante o processo de computação.

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Ariel Riera

Director de proyecto en Clarín | IT Project Implementation
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SMPC aporta significativos beneficios en la preservación de la privacidad y seguridad de datos. Facilita la colaboración sin revelar información sensible, permitiendo cálculos sobre datos compartidos sin exponer detalles. Además, minimiza riesgos asociados a la centralización de datos, otorgando control a cada entidad participante y reduciendo la vulnerabilidad a violaciones de seguridad. La capacidad de análisis conjunto sin compartir datos originales es esencial en entornos sensibles. SMPC también favorece la conformidad con regulaciones de privacidad, promueve resiliencia al operar descentralizadamente y fortalece la confianza en la colaboración. Estos aspectos hacen de SMPC una herramienta crucial en seguridad y privacidad.

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3 SMPC Challenges

Secure multiparty computation (SMPC) can present some challenges when it comes to distributed system security. Complexity is one issue, as SMPC involves sophisticated cryptographic protocols and algorithms that can be difficult to design, implement, and verify. Additionally, SMPC often incurs extra costs in terms of communication and computation compared to non-secure methods. Furthermore, it is vulnerable to various attacks such as collusion, corruption, interruption, or denial-of-service. These attacks can compromise the privacy, accuracy, or availability of the computation.

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Vanderlei de Jesus Barcala

LinkedIn Top Voice | Especialista em Clouds, Cibersegurança, Dados, DevOps, Serviços, Gerenciamento, ajudo empresas a impulsionar seus negócios digitalmente, tornando cada vez mais estratégicas.
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A SMPC utiliza primitivas criptográficas como compartilhamento secreto, criptografia homo mórfica e provas de conhecimento zero. Isso permite que um determinado número de participantes, cada um com dados, calcule uma função pública nesses dados, sem que nenhum participante saiba informações sobre a entrada de outro.

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Ariel Riera

Director de proyecto en Clarín | IT Project Implementation
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La Computación Segura entre Múltiples Partes (SMPC) enfrenta desafíos como la complejidad criptográfica, el costo computacional, la seguridad en la comunicación, la coordinación entre participantes, el impacto en el rendimiento, la educación y conciencia limitadas, la escalabilidad, y la gestión segura de claves. Superar estos obstáculos es crucial para aprovechar plenamente los beneficios de SMPC en entornos donde la privacidad y la seguridad de datos son prioritarias.

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4 SMPC Applications

SMPC can enable various applications that require privacy and security in distributed systems, such as data mining, machine learning, voting, auctions, and blockchain. For example, SMPC can allow multiple parties to collaboratively analyze their data without disclosing it to each other or a third party. This can enhance the quality and utility of the data while preserving the privacy and security of the parties. Additionally, SMPC can allow multiple parties to jointly train and test a machine learning model without revealing their data or the model parameters, improving accuracy and generalization while protecting intellectual property. Furthermore, SMPC can enable secure and fair voting processes without revealing votes or outcomes, ensuring integrity and anonymity. Moreover, SMPC can allow multiple parties to participate in secure and efficient auctions without revealing bids or winners, guaranteeing rationality and fairness. Finally, SMPC can allow multiple parties to execute smart contracts on a blockchain without revealing inputs or contract logic, enabling more complex transactions while maintaining decentralization and security.

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Vanderlei de Jesus Barcala

LinkedIn Top Voice | Especialista em Clouds, Cibersegurança, Dados, DevOps, Serviços, Gerenciamento, ajudo empresas a impulsionar seus negócios digitalmente, tornando cada vez mais estratégicas.
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Nenhuma parte pode ver ou deduzir entradas privadas de qualquer outra parte. As partes que se desviam das instruções do protocolo não podem forçar participantes honestos a produzir um resultado incorreto.

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Ariel Riera

Director de proyecto en Clarín | IT Project Implementation
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SMPC encuentra aplicación en procesamiento en la nube, investigación colaborativa, servicios financieros, salud, aprendizaje automático, cadena de suministro, votación segura, blockchain, seguros, y gobierno. Permite colaboración sin revelar datos sensibles, respaldando análisis y transacciones seguras en entornos donde la privacidad es crítica.

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5 SMPC Tools

SMPC is an active and evolving research area, and there are several tools and frameworks that can help you use SMPC in distributed system security. MPyC is a Python library that supports secure multi-party computation over finite fields, integers, and fixed-point numbers. It allows you to write Python programs using SMPC primitives such as secret sharing, arithmetic operations, comparison, and randomization. SCALE-MAMBA is a C++ framework that supports secure multi-party computation over finite fields, integers, and binary circuits. It allows you to write C++ programs using SMPC primitives such as secret sharing, arithmetic operations, bitwise operations, and branching. ABY is also a C++ framework that supports secure multi-party computation over binary circuits. It allows you to write C++ programs using SMPC primitives such as secret sharing, bitwise operations, and conversion. All of these tools provide some SMPC protocols like secure sorting, secure shuffling, secure aggregation, secure multiplication, secure comparison, secure equality, secure addition, secure multiplication and secure inner product.

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Ariel Riera

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Las herramientas para Computación Segura entre Múltiples Partes (SMPC) son fundamentales para implementar de manera eficaz y segura este paradigma. Entre ellas, PySyft destaca al facilitar la construcción de modelos de aprendizaje automático sin compartir datos crudos. Microsoft SEAL y TenSEAL ofrecen criptografía homomórfica eficiente, crucial para cálculos seguros. PySyft Keras permite integrar SMPC con el marco Keras en aprendizaje automático. SCALE-MAMBA y Charm Crypto son marcos de trabajo de código abierto para SMPC, mientras que IBM Homomorphic Encryption Toolkit y Google's Private Join and Compute ofrecen implementaciones específicas para cifrado homomórfico y privacidad en cálculos de datos, respectivamente.

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6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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Vanderlei de Jesus Barcala

LinkedIn Top Voice | Especialista em Clouds, Cibersegurança, Dados, DevOps, Serviços, Gerenciamento, ajudo empresas a impulsionar seus negócios digitalmente, tornando cada vez mais estratégicas.
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A história da Computação Multipartidária (MPC) remonta ao final da década de 1970, com a introdução de protocolos de finalidade específica para tarefas específicas. Desde então, houve um foco em aumentar a eficiência e a praticidade desses protocolos para votação, assinaturas, leilões e muito mais. No final da década de 2010, os custodiantes de ativos digitais começaram a usar o MPC para proteger ativos digitais. Para promover ainda mais a tecnologia MPC, empresas especializadas estabeleceram a MPC Alliance em 2020.

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How can you use secure multi-party computation in Distributed System Security?

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1 SMPC Basics

2 SMPC Benefits

3 SMPC Challenges

4 SMPC Applications

5 SMPC Tools

6 Here’s what else to consider

Operating Systems

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How can you use secure multi-party computation in Distributed System Security?

1

2

3

4

5

6

1 SMPC Basics

2 SMPC Benefits

3 SMPC Challenges

4 SMPC Applications

5 SMPC Tools

6 Here’s what else to consider

Operating Systems

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