Secure Computation and Trustless Data Intermediaries in Data Spaces
- URL: http://arxiv.org/abs/2410.16442v1
- Date: Mon, 21 Oct 2024 19:10:53 GMT
- Title: Secure Computation and Trustless Data Intermediaries in Data Spaces
- Authors: Christoph Fabianek, Stephan Krenn, Thomas Loruenser, Veronika Siska,
- Abstract summary: This paper explores the integration of advanced cryptographic techniques for secure computation in data spaces.
We exploit the introduced secure methods, i.e. Secure Multi-Party Computation (MPC) and Fully Homomorphic Encryption (FHE)
We present solutions through real-world use cases, including air traffic management, manufacturing, and secondary data use.
- Score: 0.44998333629984877
- License:
- Abstract: This paper explores the integration of advanced cryptographic techniques for secure computation in data spaces to enable secure and trusted data sharing, which is essential for the evolving data economy. In addition, the paper examines the role of data intermediaries, as outlined in the EU Data Governance Act, in data spaces and specifically introduces the idea of trustless intermediaries that do not have access to their users' data. Therefore, we exploit the introduced secure computation methods, i.e. Secure Multi-Party Computation (MPC) and Fully Homomorphic Encryption (FHE), and discuss the security benefits. Overall, we identify and address key challenges for integration, focusing on areas such as identity management, policy enforcement, node selection, and access control, and present solutions through real-world use cases, including air traffic management, manufacturing, and secondary data use. Furthermore, through the analysis of practical applications, this work proposes a comprehensive framework for the implementation and standardization of secure computing technologies in dynamic, trustless data environments, paving the way for future research and development of a secure and interoperable data ecosystem.
Related papers
- Collection, usage and privacy of mobility data in the enterprise and public administrations [55.2480439325792]
Security measures such as anonymization are needed to protect individuals' privacy.
Within our study, we conducted expert interviews to gain insights into practices in the field.
We survey privacy-enhancing methods in use, which generally do not comply with state-of-the-art standards of differential privacy.
arXiv Detail & Related papers (2024-07-04T08:29:27Z) - CaPS: Collaborative and Private Synthetic Data Generation from Distributed Sources [5.898893619901382]
We propose a framework for the collaborative and private generation of synthetic data from distributed data holders.
We replace the trusted aggregator with secure multi-party computation protocols and output privacy via differential privacy (DP)
We demonstrate the applicability and scalability of our approach for the state-of-the-art select-measure-generate algorithms MWEM+PGM and AIM.
arXiv Detail & Related papers (2024-02-13T17:26:32Z) - A Blockchain-based Model for Securing Data Pipeline in a Heterogeneous
Information System [0.0]
This article presents a blockchain-based model for securing data pipelines in a heterogeneous information system.
The model is designed to ensure data integrity, confidentiality, and authenticity in a decentralized manner.
arXiv Detail & Related papers (2024-01-17T14:40:09Z) - The Security and Privacy of Mobile Edge Computing: An Artificial Intelligence Perspective [64.36680481458868]
Mobile Edge Computing (MEC) is a new computing paradigm that enables cloud computing and information technology (IT) services to be delivered at the network's edge.
This paper provides a survey of security and privacy in MEC from the perspective of Artificial Intelligence (AI)
We focus on new security and privacy issues, as well as potential solutions from the viewpoints of AI.
arXiv Detail & Related papers (2024-01-03T07:47:22Z) - Securing Data Platforms: Strategic Masking Techniques for Privacy and
Security for B2B Enterprise Data [0.0]
Business-to-business (B2B) enterprises are increasingly constructing data platforms.
It has become critical to design these data platforms with mechanisms that inherently support data privacy and security.
Data masking stands out as a vital feature of data platform architecture.
arXiv Detail & Related papers (2023-12-06T05:04:37Z) - Blockchain-empowered Federated Learning for Healthcare Metaverses:
User-centric Incentive Mechanism with Optimal Data Freshness [66.3982155172418]
We first design a user-centric privacy-preserving framework based on decentralized Federated Learning (FL) for healthcare metaverses.
We then utilize Age of Information (AoI) as an effective data-freshness metric and propose an AoI-based contract theory model under Prospect Theory (PT) to motivate sensing data sharing.
arXiv Detail & Related papers (2023-07-29T12:54:03Z) - Auditing and Generating Synthetic Data with Controllable Trust Trade-offs [54.262044436203965]
We introduce a holistic auditing framework that comprehensively evaluates synthetic datasets and AI models.
It focuses on preventing bias and discrimination, ensures fidelity to the source data, assesses utility, robustness, and privacy preservation.
We demonstrate the framework's effectiveness by auditing various generative models across diverse use cases.
arXiv Detail & Related papers (2023-04-21T09:03:18Z) - A Survey of Secure Computation Using Trusted Execution Environments [80.58996305474842]
This article provides a systematic review and comparison of TEE-based secure computation protocols.
We first propose a taxonomy that classifies secure computation protocols into three major categories, namely secure outsourced computation, secure distributed computation and secure multi-party computation.
Based on these criteria, we review, discuss and compare the state-of-the-art TEE-based secure computation protocols for both general-purpose computation functions and special-purpose ones.
arXiv Detail & Related papers (2023-02-23T16:33:56Z) - Distributed Machine Learning and the Semblance of Trust [66.1227776348216]
Federated Learning (FL) allows the data owner to maintain data governance and perform model training locally without having to share their data.
FL and related techniques are often described as privacy-preserving.
We explain why this term is not appropriate and outline the risks associated with over-reliance on protocols that were not designed with formal definitions of privacy in mind.
arXiv Detail & Related papers (2021-12-21T08:44:05Z) - A big data intelligence marketplace and secure analytics experimentation
platform for the aviation industry [0.0]
This paper introduces the ICARUS big data-enabled platform that offers a novel aviation data and intelligence marketplace.
It holistically handles the complete big data lifecycle from the data collection, data curation and data exploration to the data integration and data analysis.
arXiv Detail & Related papers (2021-11-18T18:51:40Z) - A Secure Experimentation Sandbox for the design and execution of trusted
and secure analytics in the aviation domain [0.0]
ICARUS platform aims to become an 'one-stop shop' for aviation data and intelligence marketplace.
Secure Experimentation Sandbox has been designed and integrated in the ICARUS platform offering.
arXiv Detail & Related papers (2021-11-18T18:44:29Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.