SPARR: Spintronics-based private aggregatable randomized response for crowdsourced data collection and analysis

The rapid development of information and communication technologies has revolutionized people's lives. A large amount of user behavioral data is generated by client software and is valuable for organizations (e.g., Apple, Google, and Samsung) to adjust their commercial strategies or to promote the quality of service. However, data collection raises privacy concerns. Either malicious or accidental leaking of sensitive information may cause serious consequences. Randomized response is a candidate solution, and it can provide “plausible deniability” for each individual. The aggregators can only collect sanitized data, but they can still conduct analyses and make predictions. Existing randomized response solutions can either apply pseudo-randomized functions with a predicable period of random numbers or have severely restricted functionality rather than providing practical privacy protection and efficient usability. We propose the spintronics-based private aggregatable randomized response (SPARR) for crowdsourced data collection while satisfying differential privacy. SPARR utilizes a spintronics-based true random number generator and multilayer randomized response to guarantee true data randomness and high data utility. Through multilayer randomized response and the corresponding subtle decoding algorithm, SPARR can obtain exceptionally high accuracy and efficiency in the frequency estimation of client strings even for small collections of applications, compared to state-of-the-art methods.