Essays about: "privacy preserving"
Showing result 1 - 5 of 57 essays containing the words privacy preserving.
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1. Attack Strategies in Federated Learning for Regression Models : A Comparative Analysis with Classification Models
University essay from Umeå universitet/Institutionen för datavetenskapAbstract : Federated Learning (FL) has emerged as a promising approach for decentralized model training across multiple devices, while still preserving data privacy. Previous research has predominantly concentrated on classification tasks in FL settings, leaving a noticeable gap in FL research specifically for regression models. READ MORE
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2. Measuring the Utility of Synthetic Data : An Empirical Evaluation of Population Fidelity Measures as Indicators of Synthetic Data Utility in Classification Tasks
University essay from Karlstads universitet/Institutionen för matematik och datavetenskap (from 2013)Abstract : In the era of data-driven decision-making and innovation, synthetic data serves as a promising tool that bridges the need for vast datasets in machine learning (ML) and the imperative necessity of data privacy. By simulating real-world data while preserving privacy, synthetic data generators have become more prevalent instruments in AI and ML development. READ MORE
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3. Attack Strategies in Federated Learning for Regression Models : A Comparative Analysis with Classification Models
University essay from Umeå universitet/Institutionen för tillämpad fysik och elektronikAbstract : Federated Learning (FL) has emerged as a promising approach for decentralized model training across multiple devices, while still preserving data privacy. Previous research has predominantly concentrated on classification tasks in FL settings, leaving a noticeable gap in FL research specifically for regression models. READ MORE
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4. A type-driven approach for sensitivity checking with branching
University essay from Göteborgs universitet/Institutionen för data- och informationsteknikAbstract : Differential Privacy (DP) is a promising approach to allow privacy preserving statistics over large datasets of sensitive data. It works by adding random noise to the result of the analytics. Understanding the sensitivity of a query is key to add the right amount of noise capable of protecting privacy of individuals in the dataset. READ MORE
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5. Detecting Distracted Drivers using a Federated Computer Vision Model : With the Help of Federated Learning
University essay from Mittuniversitetet/Institutionen för data- och elektroteknik (2023-)Abstract : En av de vanligaste distraktionerna under bilkörning är utförandet av aktiviteter som avlägsnar förarens fokus från vägen, exempelvis användandet av en telefon för att skicka meddelanden. Det finns många olika sätt att hantera dessa problem, varav en teknik är att använda maskininlärning för att identifiera och notifiera distraherade bilförare. READ MORE
