With SMPC or a zero-knowledge proof (ZKP), an algorithm returning some single answer or truth value can be run on someone else’s data without ever needing to see that data, potentially allowing for verification or proof of some underlying question. With SMPC, machine learning models can be trained over the input data from multiple entities, resulting in a model that all users can benefit from without leaking the input data from any particular entity. Techniques like these allow for several potential use cases in which the security of data is essential. Secure enclaves allow computation to take place in a separate and heavily blocked-off section of a CPU. With SMPC, multiple entities collaboratively compute over distributed data such that no party is able to directly view any others’ original data.
HE allows algorithms and mathematical operations to be conducted directly on the encrypted data instead of first decrypting it. This includes ideas like homomorphic encryption (HE), secure more » multiparty computation (SMPC), and secure enclaves. Privacy-preserving machine learning (PPML) consists of security-focused techniques that allow data analytics and machine learning algorithms to run on sensitive data without revealing it. Allowing computation over private data without compromising its security therefore has value for safeguards inspections and analysis.
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Data analytics and machine learning to support inspections require large amounts of data that nuclear facility operators may consider proprietary or sensitive, so the IAEA may not have full access. In international nuclear safeguards, the International Atomic Energy Agency (IAEA) is tasked with inspecting and verifying nuclear facilities and their activities.
The paper focuses on the current status of the Mexican NREN: CUDI, its future organization and political support, necessary to provide seamless, large scale, high-speed, low latency and secure national backbone and international links providing connectivity, advanced network services and technical support to VCs and their e-infrastructures in collaborative R&E projects within the country, the LAC region and worldwide. computer and information systems, middleware, tools, instruments, data sets, repositories, applications and services) building e-infrastructures necessary to meet the collaborative projects’ goals. NRENs have become the mainstream of communication and collaboration to integrate the efforts of large geographically distributed teams of Virtual Communities (VC) that - train, teach and learn-, -create, use and share- knowledge, data, information more » and communications technology resources (e.g. This paper describes the role of National Research and Education Networks (NRENs) and their aim to provide a seamless, large scale, high-speed, low-latency network to accelerate scientific and education discovery for national, regional or global collaborative projects that are dependent on the use of modern high performance distributed computing systems (HPDCS) to process mathematical models, produce new knowledge and drive innovation when resolving complex problems. In this feasibility study, we discuss the challenges involved, elaborate on leveraged technologies, analyze relevant performance results and present the future vision of our work to establish secure more » collaboration capabilities within and outside of ORNL. We present a use case where scientific data generated from complex instruments, like those at the Spallation Neutron Source (SNS), are used to train a differential privacy enabled deep learning (DL) network on Summit, which is then hosted as a secure multi-party computation (MPC) service on ORNL’s Compute and Data Environment for Science (CADES) cloud computing platform for third-party inference. In this paper, we present our efforts at ORNL toward developing a secure computation platform. Several recent technological advancements have made it possible to realize these capabilities. By enabling new computing opportunities with sensitive data, we envision a secure collaborative environment that will play a significant role in accelerating scientific discovery. Devising such a secure platform is necessary for seamless scientific knowledge sharing without compromising individual, or institute-level, intellectual property and privacy details.
In a secure collaborative environment, tera-bytes of data generated from powerful scientific instruments are used to train secure machine learning (ML) models on exascale computing systems, which are then securely shared with internal or external collaborators as cloud-based services.
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