Sökning: "Confidential Machine Learning"

Visar resultat 1 - 5 av 6 uppsatser innehållade orden Confidential Machine Learning.

1. 1. Confidential Federated Learning with Homomorphic Encryption

   Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

   Författare :Zekun Wang; [2023]
   Nyckelord :Cloud Technology; Confidential Computing; Federated Learning; Homomorphic Encryption; Trusted Execution Environment; Molnteknik; Konfidentiell databehandling; Federerad inlärning; Homomorfisk kryptering; Betrodd körningsmiljö;

   Sammanfattning : 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. LÄS MER

3. 2. Research of methods and algorithms of insider detection in a computer network using machine learning technologies

   Master-uppsats, Blekinge Tekniska Högskola/Institutionen för datavetenskap

   Författare :Dmitrii Pelevin; [2021]
   Nyckelord :IPS; IDS; UBA; NoSQL; Information Security;

   Sammanfattning : Background. Security Information and Event Management (SIEM) systems today are sophisticated sets of software packages combined with hardware platforms, which can perform real-time analysis on security events and can respond to them before potential damage due to the actions of intruders. LÄS MER

4. 3. Confidential Computing in Public Clouds : Confidential Data Translations in hardware-based TEEs: Intel SGX with Occlum support

   Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

   Författare :Sri Yulianti; [2021]
   Nyckelord :TEEs; Intel SGX; Library OS; Occlum; Confidential Computing; Confidential Machine Learning; TEEs; Intel SGX; Library OS; Occlum; Konfidentiell databehandling; konfidentiellt maskininlärning;

   Sammanfattning : 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. LÄS MER

5. 4. Air Reconnaissance Analysis using Convolutional Neural Network-based Object Detection

   Master-uppsats, Mälardalens högskola/Akademin för innovation, design och teknik

   Författare :Niklas Fasth; Rasmus Hallblad; [2020]
   Nyckelord :Deep Learning; Object detection; Convolutional neural network; Faster R-CNN; Single Shot MultiBox Detector; Aerial images; Data annotation;

   Sammanfattning : The Swedish armed forces use the Single Source Intelligent Cell (SSIC), developed by Saab, for analysis of aerial reconnaissance video and report generation. The analysis can be time-consuming and demanding for a human operator. In the analysis workflow, identifying vehicles is an important part of the work. LÄS MER

7. 5. Privacy-Preserved Federated Learning : A survey of applicable machine learning algorithms in a federated environment

   Master-uppsats, Uppsala universitet/Institutionen för informationsteknologi

   Författare :Robert Carlsson; [2020]
   Nyckelord :machine learning; federated learning; privacy; preserved;

   Sammanfattning : There is a potential in the field of medicine and finance of doing collaborative machine learning. These areas gather data which can be used for developing machine learning models that could predict all from sickness in patients to acts of economical crime like fraud. LÄS MER