Privacy-Preserving Federated AI for Customer Intelligence
DOI:
https://doi.org/10.5281/zenodo.21411710Keywords:
Federated AI for Customer Intelligence, Cross-Enterprise Data Collaboration, Privacy-Preserving Machine Learning, Federated Learning Architectures, Distributed Customer Analytics Platforms, Secure Multi-Organization Data Analytics, Privacy-Enhanced AI Systems, Data Governance in Federated AI, Cross-Enterprise Risk Analytics, Collaborative AI Decision Systems, Model Inversion Attack Mitigation, Membership Inference Attack Protection, Secure Model Sharing Frameworks, Federated Data Intelligence Platforms, Confidential AI Collaboration Environments.Abstract
Cross-enterprise customer intelligence analytics seek to discover valuable insights from secure collaboration between multiple enterprises, although sensitive business data and competing interests often restrict data sharing. Federated AI enables distributed machine learning without sharing models and supporting inferencing or prediction tasks involving the aggregated intelligence of isolated, possibly heterogeneously governed datasets. The applications are crucial for risk control analytics or enriching data-driven decision systems, while cooperation can be organized in a formal consortium or on an ad-hoc basis.
Federated AI offers several potential benefits: it minimizes the issues associated with cross-enterprise data sharing and overcomes the limitations of local analysis. Nevertheless, it also introduces new privacy, security and system-level threats, such as data leakage via model sharing or model inversion and membership inference attacks against sensitive model information. Exploring the federated AI paradigm for cross-enterprise customer intelligence analytics is essential for determining its applicability, identifying missing privacy-preserving mechanisms and addressing latent security risks. These aspects can thus be analysed and adapted into a privacy-preserving security-enhanced federated AI platform proposed through design science research. Realizing and endorsing the potential advantages in a production environment, however, requires further exploration.
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