Skip to content
aiqbench takeAI Safety

aiqbench analysis

This research proposes a secure AI watermarking framework designed specifically for multi-tenant cloud environments, addressing critical IP leakage risks in federated and remote learning algorithms. By distributing cryptographic keys among trusted parties, the system aims to proactively detect and prevent unauthorized data exfiltration during model exchanges without compromising service availability. The work sits at the intersection of cryptography (cs.CR) and artificial intelligence (cs.AI), focusing on safeguarding proprietary assets within complex cloud infrastructures.
Source coveringarXiv
What this isA model's reading of the article and related coverage, written for aiqbench. Not the publisher's words.

Secure AI Watermarking Framework for IP Protection in Multi-Tenant Cloud Platforms

arXiv2 min read

arXiv:2608.02656v1 Announce Type: cross Abstract: The Secured data safe guard transaction with multi-tenant environments run on private-protected authenticate platforms runs by secured handed environments that emerges with the expansion of cloud-based AI services. To enhanced this secured leakage address challenges solution to protect a secure AI Watermarking system incorporating key distributed between trusted parties based on key authentication as we proposed solution to guided safe guarded way to reactive, and proactive security alert systems using algorithms. This proposed system before attacks can be prevented through the active measures. domain run base restrictions with limited access. Conversely, Proposed system reactive methods to captured on watermarking and biometric identification owner device specific IP leakage that occur during the exchange of data and models in federated and remote learning algorithms.

Abstract:The Secured data safe guard transaction with multi-tenant environments run on private-protected authenticate platforms runs by secured handed environments that emerges with the expansion of cloud-based AI services. To enhanced this secured leakage address challenges solution to protect a secure AI Watermarking system incorporating key distributed between trusted parties based on key authentication as we proposed solution to guided safe guarded way to reactive, and proactive security alert systems using algorithms. This proposed system before attacks can be prevented through the active measures. domain run base restrictions with limited access. Conversely, Proposed system reactive methods to captured on watermarking and biometric identification owner device specific IP leakage that occur during the exchange of data and models in federated and remote learning algorithms.

Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI)

arXiv:2608.02656 [cs.CR]

(or arXiv:2608.02656v1 [cs.CR] for this version)

https://doi.org/10.48550/arXiv.2608.02656 Focus to learn more arXiv-issued DOI via DataCite

https://doi.org/10.63328/IJRDES-V7RI6P9 Focus to learn more DOI(s) linking to related resources

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.

Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.

Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.

Full text under the publisher's open licence · 228 words

Related coverage