Special Issue on Privacy-Preserving Machine and Deep Learning

IEEE Computational Intelligence Magazine

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Aims and Scope

Privacy-Preserving Machine and Deep Learning (PP-MDL) is an emerging field that focuses on developing AI models that safeguard user data while maintaining high utility. This is achieved through a combination of techniques such as Differential Privacy (DP), Homomorphic Encryption (HE), and Federated Learning (FL).

Balancing privacy, efficiency, and model accuracy remains a core challenge in PP-MDL. Privacy-enhancing techniques often introduce trade-offs, such as increased computational overhead or reduced model accuracy, driving ongoing research toward more scalable, secure, and privacy-aware AI solutions.

Innovations in cryptographic methods, secure multiparty computation (MPC), and privacy-preserving neural architectures aim to bridge this gap, making privacy-respecting AI more practical for real-world applications.

Topics of Interest

Submission Guidelines

The IEEE Computational Intelligence Magazine (CIM) publishes peer-reviewed, high-quality articles. All manuscripts must be submitted electronically in PDF format.

Submission Link: (to be added)

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