Privacy-Preserving AI in SMB Customer Service: Balancing Data Isolation, Compliance, and Automation Efficacy
DOI:
https://doi.org/10.71222/jm98nt46Keywords:
privacy-preserving AI, federated learning, differential privacy, homomorphic encryption, customer service automation, SMB, GDPR, CCPAAbstract
This research investigates the application of Privacy-Preserving AI (PPAI) techniques in Small and Medium-sized Business (SMB) customer service environments, recognizing that SMBs constitute the backbone of the U.S. economy. It focuses on balancing the seemingly conflicting demands of data isolation, regulatory compliance (e.g., GDPR, CCPA), and the efficacy of AI-driven automation. The study explores various PPAI methodologies, including Federated Learning, Differential Privacy, Homomorphic Encryption, and Secure Multi-Party Computation, assessing their suitability for different SMB customer service scenarios. We analyze the trade-offs between privacy guarantees, model accuracy, computational overhead, and implementation complexity. Real-world case studies and simulations are used to evaluate the performance of selected PPAI techniques across key customer service metrics such as response time, customer satisfaction, and issue resolution rate. Furthermore, the research addresses the practical challenges of deploying PPAI in resource-constrained SMBs, considering factors like data heterogeneity, limited technical expertise, and cost considerations. This study proposes a framework for SMBs to effectively adopt PPAI in their customer service operations, ensuring robust data protection, adherence to compliance regulations, and enhanced automation capabilities. By lowering the technical barrier for SMBs to adopt AI while strictly adhering to privacy laws, this work aims to support widespread economic modernization alongside robust consumer privacy protection. The findings provide valuable insights for SMBs, AI developers, and policymakers aiming to promote the responsible and ethical use of AI in customer service.References
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Copyright (c) 2026 Zhenyuan He (Author)
This work is licensed under a Creative Commons Attribution 4.0 International License.
