Accelerate Secure Collaboration with Messageless Computation
Messageless Computation is an advanced form of multiparty computation that allows secure processing between multiple parties without the need for explicit message exchanges. This innovative approach significantly speeds up the computational process while maintaining privacy and security. Enhance your collaborative efforts by integrating Messageless Computation, and achieve faster, more efficient outcomes without compromising data confidentiality.
What is Messageless Computation and How Does It Work?
Messageless Computation is a cryptographic protocol that allows multiple parties to perform computations without exchanging messages during the process. Developed by Sedicii, it uses pre-distributed data or shared randomness to speed up computations and reduce communication overhead, making it ideal for secure and privacy-preserving data analysis.
Initial Setup
Participants agree on a common computational task or function that needs to be computed jointly. They may also agree on cryptographic protocols or algorithms to be used for the computation.
Pre-processing Phase
Participants independently preprocess their inputs and generate local information or data structures that will be used during the computation. This preprocessing phase allows each participant to prepare their local resources without the need for direct communication with others.
Computation Phase
Each participant performs local computations using their preprocessed inputs and local data structures. These computations are designed such that they can be executed independently and in parallel by each participant, without requiring any exchange of intermediate results or messages between them.
Final Aggregation Phase
After completing their local computations, participants aggregate their individual results to compute the final output or result of the joint computation. Depending on the protocol used, aggregation may involve simple operations like addition or more complex techniques such as secure multiparty computation (MPC) protocols.
Reduced Communication Overhead
By eliminating the need for real-time message exchanges during the computation phase, messageless computation reduces communication latency and bandwidth requirements.
Privacy-preserving Data Minding
Participants maintain the privacy of their inputs and intermediate computations since there is no need to transmit sensitive information during the computation process.
Scalability
Messageless computation can scale very efficiently to large numbers of participants or data points, as each participant’s local computations can be performed independently and in parallel.
Revolutionise Your Operations with Messageless Computation
Messageless Computation (MLC) offers key benefits, including decentralisation, fault tolerance, and enhanced security. Its fast processing and scalability make it ideal for large-scale, privacy-focused collaborative applications. Discover how MLC can revolutionise data processing and secure collaboration in diverse environments.
MLC is decentralised, secure & fault tolerant
MLC is decentralised because there is no lead party. Therefore it is resistant to corruption. There’s also no single point of failure which makes it fault tolerant. And finally, it’s secure because the MLC nodes in the computation don’t see the information they are processing because it has been masked by the OTM protocol.
Processing is fast
MLC is efficient because the MLC nodes don’t waste time exchanging lots of messages in order to run a computation. Consequently MLC can be used as a key piece of storage and processing infrastructure that can be used to build the next generation of collaborative applications that require privacy and security at their core.
Scalability
MLC allows computations to scale efficiently with the number of participants or data points involved. Each participant can independently process their local data and contribute to the joint computation without coordination overhead. This scalability is crucial for handling large-scale data analytics, decentralised processing tasks, and collaborative computations across diverse environments.
FAQs
Get answers to common questions about our services and technology. Our FAQ section provides quick, clear information to help you understand how we can support your organisation.
What are some good use cases for MLC?
This approach can significantly enhance privacy and security, as it minimises the risk of data leakage during communication. Here are some promising use cases for messageless computation: Privacy-Preserving Machine Learning, Secure Voting Systems, Collaborative Data Analysis, Secure Auctions and Bidding, Financial Transactions and Risk Assessment, Supply Chain Management, Data Monetisation, Blockchain and Smart Contracts, Healthcare Data Sharing, Anonymized Credential Verification.
What is the key innovation in MLC?
The key innovation in messageless computation is the ability to perform secure and efficient computations without the need for communication between parties. By eliminating the risks associated with data transmission, messageless computation enhances privacy, reduces latency, and supports scalable collaborations. These innovations are particularly valuable in applications requiring high levels of confidentiality and security, making messageless computation a powerful tool in the landscape of privacy-preserving technologies.
What is Information Theoretic Security?
Information Theoretic Security (ITS) is a concept in cryptography that focuses on providing security guarantees based on the principles of information theory, rather than relying solely on computational assumptions. This approach aims to ensure that a cryptographic system is secure against any potential adversary, regardless of their computational power.
Are there security implications of using MLC?
While messageless computation offers enhanced privacy and security by eliminating direct communication, it is not without its risks and challenges. Understanding and addressing these security implications is critical for effectively implementing messageless computation protocols in practice. Robust protocol design, thorough testing, and a clear understanding of the underlying assumptions are essential to mitigate potential vulnerabilities and ensure the security of the system.
Can the output of a computation reveal anything of the input data?
Yes, the output of a computation can potentially reveal information about the input data, depending on several factors such as the nature of the computation, the specific algorithms used, and the context in which the output is interpreted.
Project PHACKS
The Platform for High Assurance Collaborative Knowledge Sharing (PHACKS) is an EU funded commercialisation initiative aimed at developing new markets for advanced privacy-preserving technologies where data collaboration is at the core of the participants collaboration. The project focuses on creating solutions that enable secure and privacy-respecting access to digital services and resources that leverage data assets that each of the participants hold but in a manner where those assets are not disclosed to the other participants. PHACKS leverages cutting-edge cryptographic techniques, including zero-knowledge proofs and secure multiparty computation, to ensure that sensitive information is completely protected during the data processing stages.