Essays about: "Konfidentiell databehandling"
Found 4 essays containing the words Konfidentiell databehandling.
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1. 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
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2. Confidential Computing in Public Clouds : Confidential Data Translations in hardware-based TEEs: Intel SGX with Occlum support
University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)Abstract : As enterprises migrate their data to cloud infrastructure, they increasingly need a flexible, scalable, and secure marketplace for collaborative data creation, analysis, and exchange among enterprises. Security is a prominent research challenge in this context, with a specific question on how two mutually distrusting data owners can share their data. READ MORE
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3. Utility of Differentially Private Synthetic Data Generation for High-Dimensional Databases
University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)Abstract : When processing data that contains sensitive information, careful consideration is required with regard to privacy-preservation to prevent disclosure of confidential information. Privacy engineering enables one to extract valuable patterns, safely, without compromising anyone’s privacy. READ MORE
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4. Utility of Differentially Private Synthetic Data Generation for High-Dimensional Databases
University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)Abstract : When processing data that contains sensitive information, careful consideration is required with regard to privacy-preservation to prevent disclosure of confidential information. Privacy engineering enables one to extract valuable patterns, safely, without compromising anyone’s privacy. READ MORE