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Found 9 papers, 0 papers with code
Differentially Private Publication of Electricity Time Series Data in Smart Grids
We introduce {\em STPT (Spatio-Temporal Private Timeseries)}, a novel method for DP-compliant publication of electricity consumption data that analyzes spatio-temporal attributes and captures both micro and macro patterns by leveraging RNNs.
Holistic Survey of Privacy and Fairness in Machine Learning
Privacy and fairness are two crucial pillars of responsible Artificial Intelligence (AI) and trustworthy Machine Learning (ML).
Learning Dynamic Graphs from All Contextual Information for Accurate Point-of-Interest Visit Forecasting
Forecasting the number of visits to Points-of-Interest (POI) in an urban area is critical for planning and decision-making for various application domains, from urban planning and transportation management to public health and social studies.
Fair Spatial Indexing: A paradigm for Group Spatial Fairness
We propose techniques to mitigate location bias in machine learning.
Models and Mechanisms for Spatial Data Fairness
We introduce the concept of spatial data fairness to address the specific challenges of location data and spatial queries.
Multi-modal Misinformation Detection: Approaches, Challenges and Opportunities
As social media platforms are evolving from text-based forums into multi-modal environments, the nature of misinformation in social media is also transforming accordingly.
When Machine Learning Meets Privacy: A Survey and Outlook
The newly emerged machine learning (e. g. deep learning) methods have become a strong driving force to revolutionize a wide range of industries, such as smart healthcare, financial technology, and surveillance systems.
Privacy Preserving Location Data Publishing: A Machine Learning Approach
By introducing a new formulation of the problem, we are able to apply machine learning algorithms for clustering the trajectories and propose to use $k$-means algorithm for this purpose.
Privacy Preservation in Location-Based Services: A Novel Metric and Attack Model
In this paper, we incorporate a new type of side information based on consecutive location changes of users and propose a new metric called transition-entropy to investigate the location privacy preservation, followed by two algorithms to improve the transition-entropy for a given dummy generation algorithm.
