Daniel Takabi

Orcid: 0000-0003-0447-3641

According to our database1, Daniel Takabi authored at least 27 papers between 2019 and 2024.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

Legend:

Book 
In proceedings 
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PhD thesis 
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Links

On csauthors.net:

Bibliography

2024
Self-Supervised Learning for Near-Wild Cognitive Workload Estimation.
J. Medical Syst., December, 2024

Self-Supervised Learning for Electroencephalography.
IEEE Trans. Neural Networks Learn. Syst., February, 2024

A Semantic, Syntactic, and Context-Aware Natural Language Adversarial Example Generator.
IEEE Trans. Dependable Secur. Comput., 2024

Privacy-Preserving Machine Learning Using Functional Encryption: Opportunities and Challenges.
IEEE Internet Things J., 2024

SSCAE - Semantic, Syntactic, and Context-aware natural language Adversarial Examples generator.
CoRR, 2024

I can't see it but I can Fine-tune it: On Encrypted Fine-tuning of Transformers using Fully Homomorphic Encryption.
CoRR, 2024

MedBlindTuner: Towards Privacy-preserving Fine-tuning on Biomedical Images with Transformers and Fully Homomorphic Encryption.
CoRR, 2024

A Systematic Mapping Study on Intrusion Response Systems.
IEEE Access, 2024

RobustSentEmbed: Robust Sentence Embeddings Using Adversarial Self-Supervised Contrastive Learning.
Proceedings of the Findings of the Association for Computational Linguistics: NAACL 2024, 2024

Memory Efficient Privacy-Preserving Machine Learning Based on Homomorphic Encryption.
Proceedings of the Applied Cryptography and Network Security, 2024

2023
Towards Neural Network-Based Communication System: Attack and Defense.
IEEE Trans. Dependable Secur. Comput., 2023

An Insider Threat Mitigation Framework Using Attribute Based Access Control.
CoRR, 2023

RobustEmbed: Robust Sentence Embeddings Using Self-Supervised Contrastive Pre-Training.
Proceedings of the Findings of the Association for Computational Linguistics: EMNLP 2023, 2023

FENet: Privacy-preserving Neural Network Training with Functional Encryption.
Proceedings of the 9th ACM International Workshop on Security and Privacy Analytics, 2023

2022
Privacy Threat and Defense for Federated Learning With Non-i.i.d. Data in AIoT.
IEEE Trans. Ind. Informatics, 2022

NeuroCrypt: Machine Learning Over Encrypted Distributed Neuroimaging Data.
Neuroinformatics, 2022

Audio-Visual Autoencoding for Privacy-Preserving Video Streaming.
IEEE Internet Things J., 2022

SoK: Privacy Preserving Machine Learning using Functional Encryption: Opportunities and Challenges.
CoRR, 2022

A Survey of Deep Learning Architectures for Privacy-Preserving Machine Learning With Fully Homomorphic Encryption.
IEEE Access, 2022

Integrating Cyber Deception Into Attribute-Based Access Control (ABAC) for Insider Threat Detection.
IEEE Access, 2022

2021
SoK: Privacy-preserving Deep Learning with Homomorphic Encryption.
CoRR, 2021

Towards Faster Functional Encryption for Privacy-preserving Machine Learning.
Proceedings of the 3rd IEEE International Conference on Trust, 2021

Non-interactive Privacy Preserving Recurrent Neural Network Prediction with Homomorphic Encryption.
Proceedings of the 14th IEEE International Conference on Cloud Computing, 2021

2020
Effective Activation Functions for Homomorphic Evaluation of Deep Neural Networks.
IEEE Access, 2020

Classification of Encrypted Word Embeddings using Recurrent Neural Networks.
Proceedings of the PrivateNLP 2020: Workshop on Privacy in Natural Language Processing, 2020

A Hybrid Policy Engineering Approach for Attribute-Based Access Control (ABAC).
Proceedings of the 12th International Conference on Soft Computing and Pattern Recognition (SoCPaR 2020), 2020

2019
Privacy preserving Neural Network Inference on Encrypted Data with GPUs.
CoRR, 2019


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