Sökning: "Homomorphic encryption"

Visar resultat 1 - 5 av 15 uppsatser innehållade orden Homomorphic encryption.

1. 1. XZDDF Bootstrapping in Fully Homomorphic Encryption

   Master-uppsats, Lunds universitet/Institutionen för elektro- och informationsteknik

   Författare :Simon Ljungbeck; [2024]
   Nyckelord :Fully Homomorphic Encryption; Homomorphic Encryption; Bootstrapping; Blind Rotation; XZDDF; Technology and Engineering;

   Sammanfattning : Despite the vast research on the topic in recent years, fully homomorphic encryption schemes remain time-inefficient. The main bottleneck is the so-called bootstrapping, whose purpose is to reduce noise that has accumulated after having performed homomorphic operations on a ciphertext. LÄS MER

3. 2. Preserving Privacy in Cloud Services by Using an Explainable Deep-Learning Model for Anomaly Detection

   Master-uppsats, Linköpings universitet/Institutionen för datavetenskap

   Författare :Shiwei Dong; [2023]
   Nyckelord :;

   Sammanfattning : 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. LÄS MER

4. 3. Homomorphic encryption

   Master-uppsats, KTH/Matematik (Avd.)

   Författare :Daniel Sanaee; [2023]
   Nyckelord :Mathematics; Cryptography; Computer science; Group Theory; Matematik; Kryptografi; Datalogi; Gruppteori;

   Sammanfattning : The problem of constructing a secure encryption scheme that allows for computation on encrypted data was an open problem for more than 30 years. In 2009, Craig Gentry solved the problem, constructing the first fully homomorphic encryption (FHE) scheme. LÄS MER

5. 4. 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

7. 5. Privacy Preserving Biometric Multi-factor Authentication

   Master-uppsats, Lunds universitet/Institutionen för elektro- och informationsteknik

   Författare :Emil Gedenryd; [2023]
   Nyckelord :Fully Homomorphic Encryption; Machine Learning; Biometric Authentication; Technology and Engineering;

   Sammanfattning : This thesis investigates the viability of using Fully Homomorphic Encryption and Machine Learning to construct a privacy-preserving biometric multi-factor authentication system. The system is based on the architecture described as ”Model K - Store distributed, compare distributed” in ISO/IEC 24745:2022 and uses the Torus Fully Homomorphic Encryption scheme proposed by Chillotti et al. LÄS MER