Essays about: "Privacy-preserving"
Showing result 1 - 5 of 44 essays containing the word Privacy-preserving.
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1. 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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2. Preserving Privacy in Cloud Services by Using an Explainable Deep-Learning Model for Anomaly Detection
University essay from Linköpings universitet/Institutionen för datavetenskapAbstract : As cloud services become increasingly popular, ensuring their privacy and security has become a significant concern for users. Cloud computing involves Data Service Outsourcing and Computation Outsourcing, which require additional security considerations compared to traditional computing. READ MORE
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3. Privacy-preserving Authentication in Participatory Sensing Systems : An attribute based authentication solution with sensor requirement enforcement.
University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)Abstract : Participatory Sensing Systems (PSS) are a type of Mobile Crowdsensing System where users voluntarily participate in contributing information. Task initiators create tasks, targeting specific data that needs to be gathered by the users’ device sensors. READ MORE
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4. Towards Building Privacy-Preserving Language Models: Challenges and Insights in Adapting PrivGAN for Generation of Synthetic Clinical Text
University essay from Stockholms universitet/Institutionen för data- och systemvetenskapAbstract : The growing development of artificial intelligence (AI), particularly neural networks, is transforming applications of AI in healthcare, yet it raises significant privacy concerns due to potential data leakage. As neural networks memorise training data, they may inadvertently expose sensitive clinical data to privacy breaches, which can engender serious repercussions like identity theft, fraud, and harmful medical errors. READ MORE
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5. Generative AI for Synthetic Data
University essay from Lunds universitet/Institutionen för reglerteknikAbstract : Synthetic data generation has emerged as a valuable technique for addressing data scarcity and privacy concerns and improving machine learning algorithms. This thesis focuses on progressing the field of synthetic data generation, which may play a crucial role in AI-heavy industries such as telecommunications. READ MORE