The exponential growth of machine learning applications in healthcare, finance, and cloud computing has raised significant data privacy concerns, as traditional encryption methods require access to decrypted data for processing, creating security vulnerabilities. Regulatory frameworks including the European Union's General Data Protection Regulation and the Health Insurance Portability and Accountability Act mandate privacy protection throughout the entire data management lifecycle. This research develops a privacy-preserving machine learning framework combining CKKS-based homomorphic encryption with Logistic Regression and AdaBoost classifiers using the UCI Adult Income dataset containing 4... More >

Audio-visual spoofing attacks have emerged as a serious threat to modern hearing-assistive systems due to rapid advances in text-to-speech synthesis, neural vocoders, and lip-sync deepfake generation. Advanced hearing aids and cochlear implants increasingly incorporate AI-based speech enhancement and multimodal perception modules, which makes them vulnerable to manipulated or synthetic inputs. Traditional spoof detection approaches are often limited to binary classification between bonafide and spoofed speech, failing to capture the diversity of emerging multi-modal attack types.In this paper, we propose a multi-attack audio-visual spoof detection framework designed that explicitly models fo... More >