Abstract Microaggregation is a powerful technique for safeguarding data, enabling us to strike a balance between the risk of disclosing sensitive information and the loss of valuable information. It is a crucial tool for data sharing that provides k -anonymity. With the growing prevalence of multi-view data, there is an increasing interest in protecting such data using appropriate techniques. To the best of our knowledge, this paper introduces the first approach specifically designed for multi-view data protection. We present a novel approach to microaggregation by introducing multi-view fuzzy c-means, which allows us to consider linear constraints on the variables in each view that describe the data. Our method ensures that the resulting clusters adhere to these constraints, even when the data being masked fails to satisfy them. This approach not only enhances data privacy by maintaining k -anonymity in multi-view contexts but also preserves the structural integrity of the data across different views. Our contributions include the development of a multi-view clustering framework with built-in privacy safeguards and the introduction of linear constraints to ensure consistency across multiple views. This innovative approach provides a robust solution for the privacy-preserving analysis and sharing of multi-view data.

Abstract We study ideals and filters of posets and of pseudocomplemented posets and show a version of the Separation Theorem, known for ideals and filters in lattices and semilattices, within this general setting. We extend the concept of a $$*$$ -ideal already introduced by Rao for pseudocomplemented distributive lattices and by Talukder, Chakraborty and Begum for pseudocomplemented semilattices to pseudocomplemented posets. We derive several important properties of such ideals. Especially, we explain connections between prime filters, ultrafilters, filters satisfying the $$*$$ -condition and dense elements. Finally, we prove a Separation Theorem for $$*$$ -ideals.

Abstract This paper investigates household energy management systems that integrate renewable energy sources and battery storage, modeled as discrete-time optimization problems. Motivated by global trends toward decarbonization and recent policy initiatives promoting distributed energy resources, a data-driven method is proposed that combines fuzzy-rule networks with reinforcement learning in an actor-critic architecture. The controller adaptively regulates power demand while treating renewable energy as an uncertain disturbance. Relying only on real-time demand and battery status data, it aims to minimize electricity costs and preserve battery health. Validation addresses uncertainties in energy prices, user behavior, and environmental conditions. A virtual desired state of charge enhances operational stability, and comparative results confirm the controller’s effectiveness in reducing costs and optimizing battery performance.