Related papers: Applications of Secure Multi-Party Computation in …
Privacy preserving multi-party computation has many applications in areas such as medicine and online advertisements. In this work, we propose a framework for distributed, secure machine learning among untrusted individuals. The framework…
Multi-party learning is an indispensable technique for improving the learning performance via integrating data from multiple parties. Unfortunately, directly integrating multi-party data would not meet the privacy preserving requirements.…
In a large-scale distributed machine learning system, coded computing has attracted wide-spread attention since it can effectively alleviate the impact of stragglers. However, several emerging problems greatly limit the performance of coded…
We present a relational MPC framework for secure collaborative analytics on private data with no information leakage. Our work targets challenging use cases where data owners may not have private resources to participate in the computation,…
Cloud computing is an Internet-based computing, where shared resources, software and information, are provided to computers and devices on-demand. It provides people the way to share distributed resources and services that belong to…
Federated learning facilitates the collaborative training of models without the sharing of raw data. However, recent attacks demonstrate that simply maintaining data locality during training processes does not provide sufficient privacy…
Diffusion Models (DMs) achieve state-of-the-art synthesis results in image generation and have been applied to various fields. However, DMs sometimes seriously violate user privacy during usage, making the protection of privacy an urgent…
In recent years, the confidentiality of smart contracts has become a fundamental requirement for practical applications. While many efforts have been made to develop architectural capabilities for enforcing confidential smart contracts, a…
In this paper, we present a quantum secure multi-party summation protocol, which allows multiple mutually distrustful parties to securely compute the summation of their secret data. In the presented protocol, a semitrusted third party is…
Private computation, which includes techniques like multi-party computation and private query execution, holds great promise for enabling organizations to analyze data they and their partners hold while maintaining data subjects' privacy.…
Motivated by the importance of floating-point computations, we study the problem of securely and accurately summing many floating-point numbers. Prior work has focused on security absent accuracy or accuracy absent security, whereas our…
Increasing incidents of security compromises and privacy leakage have raised serious privacy concerns related to cyberspace. Such privacy concerns have been instrumental in the creation of several regulations and acts to restrict the…
Privacy-preserving computation techniques like homomorphic encryption (HE) and secure multi-party computation (SMPC) enhance data security by enabling processing on encrypted data. However, the significant computational and CPU-DRAM data…
Security is an important facet of integrated circuit design for many applications. IP privacy and Trojan insertion are growing threats as circuit fabrication in advanced nodes almost inevitably relies on untrusted foundries. A proposed…
We propose a novel protocol for computing a circuit which implements the multi-party private set intersection functionality (PSI). Circuit-based approach has advantages over using custom protocols to achieve this task, since many…
In this paper, we present a secure multiparty computation (SMC) protocol for least common multiple (LCM) based on Shor's quantum period-finding algorithm (QPA). Our protocol is based on the following principle: the connection of multiple…
Cloud Computing holds the potential to eliminate the requirements for setting up of high-cost computing infrastructure for the IT-based solutions and services that the industry uses. It promises to provide a flexible IT architecture,…
Secure multiparty computation enables collaborative computations across multiple users while preserving individual privacy, which has a wide range of applications in finance, machine learning and healthcare. Secure multiparty computation…
Data splitting preserves privacy by partitioning data into various fragments to be stored remotely and shared. It supports most data operations because data can be stored in clear as opposed to methods that rely on cryptography. However,…
Analytics on personal data, such as individuals' mobility, financial, and health data can be of significant benefit to society. Such data is already collected by smartphones, apps and services today, but liberal societies have so far…