Design of Orthogonal Phase of Arrival Positioning Scheme Based on 5G PRS and Optimization of TOA Performance

This study analyzes the performance of positioning techniques based on configuration changes of 5G New Radio signals. In 5G networks, a terminal position is determined from the Time of Arrival of Positioning Reference Signals transmitted by base stations. We propose an algorithm that improves TOA accuracy under low sampling rate constraints and implement 5G PRS for positioning in a software defined modem. We also examine how flexible time frequency resource allocation of PRS affects TOA estimation accuracy and discuss optimal PRS configurations for a given signal environment.

Collecting and Analyzing Multidimensional Data with Local Differential Privacy

Local differential privacy (LDP) is a recently proposed privacy standard for collecting and analyzing data, which has been used, e.g., in the Chrome browser, iOS and macOS. In LDP, each user perturbs her information locally, and only sends the randomized version to an aggregator who performs analyses, which protects both the users and the aggregator against private information leaks. Although LDP has attracted much research attention in recent years, the majority of existing work focuses on applying LDP to complex data and/or analysis tasks. In this paper, we point out that the fundamental problem of collecting multidimensional data under LDP has not been addressed sufficiently, and there remains much room for improvement even for basic tasks such as computing the mean value over a single numeric attribute under LDP. Motivated by this, we first propose novel LDP mechanisms for collecting a numeric attribute, whose accuracy is at least no worse (and usually better) than existing solutions in terms of worst-case noise variance. Then, we extend these mechanisms to multidimensional data that can contain both numeric and categorical attributes, where our mechanisms always outperform existing solutions regarding worst-case noise variance. As a case study, we apply our solutions to build an LDP-compliant stochastic gradient descent algorithm (SGD), which powers many important machine learning tasks. Experiments using real datasets confirm the effectiveness of our methods, and their advantages over existing solutions.