On the analysis of individual data on transport usage

The percentage of the world's urban population is currently more than 50\% and will increase according to UN forecasts. Urban infrastructure must develop along with population growth. This article provides an overview of methods for improving the city's transport infrastructure based on data analysis. The article presents methods for reducing harmful emissions, optimizing the operation of taxis and public transport, as well as recognizing transportation modes and some other tasks. These methods operate with data describing the transport behavior of individual users of the transport network. The sources of such data are smart card validators, GPS sensors, and smartphone accelerometers. The article reveals the advantages and disadvantages of using each of the data types, as well as presents alternative ways to obtain them. These methods, along with methods for aggregated data analysis, can become the main part of a single platform that will allow city authorities in the process of improving the transport infrastructure. We propose architecture of this platform which will allows developers to extend range of available algorithms and methods dynamically.

DOI: 10.14357/20790279230104

A Survey of Model Inversion Attacks and Countermeasures

This article provides a detailed overview of the so-called Model Inversion(MI) attacks. These attacks aim at Machine-Learning-as-a-Service (MLaaS) platforms, and the goal is to use some well-prepared adversarial samples to attack target models and gain sensitive information from ML models, such as items from the dataset on which ML model was trained or ML model's parameters. This kind of attack now becomes an enormous threat to ML models, therefore, it is necessary to research this attack, understand how it will affect ML models, and based on this knowledge, we can propose some strategies that may improve the robustness of ML models.

DOI: 10.14357/20790279230110