Essays about: "User Privacy"
Showing result 16 - 20 of 294 essays containing the words User Privacy.
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16. Cloud-Based Collaborative Local-First Software
University essay from Umeå universitet/Institutionen för datavetenskapAbstract : Local-first software has the potential to offer users a great experience by combining the best aspects of traditional applications with cloud-based applications. However, not much has been documented regarding developing backends for local-first software, particularly one that is scalable while still supporting end-to-end encryption. READ MORE
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17. A SYSTEMATIC REVIEW OF ATTRIBUTE-BASED ENCRYPTION FOR SECURE DATA SHARING IN IoT ENVIRONMENT.
University essay from Stockholms universitet/Institutionen för data- och systemvetenskapAbstract : Internet of Things (IoT) refers to a network of global and interrelated computing devices that connects humans and machines. It connects anything that has access to the internet and creates an avenue for data and information exchange. READ MORE
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18. Development guidelines for increased consumer privacy - Privacy in Home Assistants
University essay from Blekinge Tekniska Högskola/Institutionen för programvaruteknikAbstract : Research has shown that people are generally unaware of what information manufacturers gather and store about them via their IoT-devices. All they know is that there may be some collection of information. READ MORE
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19. Development of a Mobile Phone Application for Measuring Muscle Shaking (tremor) in Order to Simplify Medical Diagnostics
University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)Abstract : At the moment, there is no quick and easy method to measure muscle shaking (tremor) reliably. In order to avoid long waiting times for an Electromyography (EMG) investigation, this thesis aims to create an Android smartphone application capable of measuring muscle tremor at a moment’s notice. READ MORE
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20. Confidential Federated Learning with Homomorphic Encryption
University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)Abstract : Federated Learning (FL), one variant of Machine Learning (ML) technology, has emerged as a prevalent method for multiple parties to collaboratively train ML models in a distributed manner with the help of a central server normally supplied by a Cloud Service Provider (CSP). Nevertheless, many existing vulnerabilities pose a threat to the advantages of FL and cause potential risks to data security and privacy, such as data leakage, misuse of the central server, or the threat of eavesdroppers illicitly seeking sensitive information. READ MORE