Multidimensional data collection via interval-based perturbation under <math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="d1e2014" altimg="si88.svg" class="math"><mrow><mo>(</mo><mi>ϵ</mi><mo>,</mo><mi>δ</mi><mo>)</mo></mrow></math>-local differential privacy

This academic paper discusses a method for collecting data from multiple dimensions (different types of information) while protecting privacy using interval-based perturbation (adding controlled randomness to specific ranges of values) under differential privacy (a mathematical framework that limits how much an AI system can learn about individual data points). The research focuses on how to gather useful information while maintaining privacy guarantees.