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Information | Free Full-Text | Privacy and Security Mechanisms for B2B Data Sharing: A Conceptual Framework

In the age of digitalization, business-to-business (B2B) data sharing is becoming increasingly important, enabling organizations to collaborate and make informed decisions as well as simplifying operations and hopefully creating a cost-effective virtual value chain. This is crucial to the success of modern businesses, especially global business. However, this approach also comes with significant privacy and security challenges, thus requiring robust mechanisms to protect sensitive information. After analyzing the evolving status of B2B data sharing, the purpose of this study is to provide insights into the design of theoretical framework solutions for the field. This study adopts technologies including encryption, access control, data anonymization, and audit trails, with the common goal of striking a balance between facilitating data sharing and protecting data confidentiality as well as data integrity. In addition, emerging technologies such as homomorphic encryption, blockchain, and ....

Cheng Xu , Matt Blaze , Kete Wang , Permissioned Blockchain , Data Storage Technology , Service Daa , Research Significance , Linux Foundation , Data Encryption Technology , General Data Protection Regulation , Hyperledger Fabric , Peer To Electronic Cash System , Practical Byzantine Fault Tolerance , Data Storage , Data Encryption , Access Control , Data Access , Transaction Audit , Hyperledger Project , Privacy Preservation ,

"Data-Matching-Based Privacy-Preserving Statistics and Its Applications" by Hua Shen, Ge Wu et al.

With the rapid development of digital media technology, many people prefer to read e-books over article versions. The digital publishing platform can collect and analyze massive amounts of readers' reading information. The statistical analysis results can be regarded as the platform's digital assets based on which it provides paid services for its users. However, three privacy issues are related to readers' reading information, users' statistical preferences, and the platform's digital assets. This article proposes a data-matching-based privacy-preserving statistic scheme. The proposed solution combines bloom filters, secret sharing, and perturbing technologies to realize an efficient match between users' statistical preferences and massive readers' corresponding reading information and statistical analysis of the matching results without compromising the privacy of different parties. Besides, the proposed solution adopts an edge computing paradigm to ....

Mirror Secret Shares , Buddy Edge Devices , Bloom Filter , Data Match , Privacy Preservation , Secret Sharing , Statistical Analysis ,

Anonos Recognized in Gartner 2023 Hype Cycle Report for Generative AI

Anonos Recognized in Gartner 2023 Hype Cycle Report for Generative AI
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Arun Chandrasekaran , Leinar Ramos , Data Embassy , Gartner Inc , Hype Cycle , Improve Data Privacy , Sample Vendor , Prompt Protector , Cool Vendor , Privacy Management , Privacy Preservation , Security Management , Gartner Hype Cycle , Top Use Cases , Tabular Synthetic , Variant Twins , Aktien Sie ,