Privacy Issues in Human-Computer Interaction

Abstract

The crucial privacy issues surrounding Brain-Computer Interfaces (BCIs), which translate brain activity into commands for assistive technology, are prevalent in Human- Computer Interaction (HCI). Maintaining trust and protecting user data are critical as BCIs become more and more integrated into daily life. This work explores non-invasive interaction strategies for people with physical constraints, augmented by EEG-based BCIs, and looks at how privacy concerns affect user behavior and the HCI experience as a whole. To enhance privacy, important techniques include user profiling, secure data transfer methods, anonymization, pseudonymization, and shorter data retention periods. The study also looks into the potential for hacking and interference in wirelessly connected BCI devices. To improve security, a thorough security study and a privacy-by-design approach are suggested. The inability of conventional techniques to safeguard such data without sacrificing its analytical utility emphasizes the need for sophisticated solutions like order-preserving encryption (OPE) to strike a compromise between data privacy and usability also multi- pronged strategy that combines machine learning approaches with MATLAB-based validation, data collecting, framework creation, and literature review. An EEG privacy framework is developed and tested by the research team using the Kaggle EEG dataset. A user control layer with functions like consent management and data deletion is included in the framework together with robust security protocols and cutting-edge anonymization techniques. Advantage privacy-preserved data is reserve, as shown in Implementation and result analysis section the MATLAB-based validation. The ability of the system in reconciling privacy ammunition and data serviceability is validated using performance

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targeted and simulated assails. The consequence of secrecy for human-computer interaction, terminate that the urged privacy design successfully target a coherence between user control, data serving, and privacy, enhance the security of crucial EEG data. To ensure the moral and secure use of user data in Human-Computer Interaction, this research makes admonition for improve research projects that will increase privacy protections further in this improvement section.