MalDeXA - A Malware Detection system using XGBoost on Amazon Web Services

Thakur, Govind; Nayak, Shreya; Mangrulkar, Ramchandra

doi:10.1109/icses52305.2021.9633927

Cited by 2 publications

(2 citation statements)

References 22 publications

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“…Data augmentation is a major use of generative AI in data science, where it may be used to create fresh training instances and enhance machine learning model performance. Generative AI can assist in resolving problems such as overfitting and a shortage of training data by producing artificial intelligence (AI) that is similar to real data [10].…”

Section: Applications Of Generative Ai In Data Science and Industry 50mentioning

confidence: 99%

Frontiers of Artificial Intelligence Versus Generative, Explainable and Quantum Technologies: An Overview

2024

OAJDA

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Generative AI can produce content that is similar to human ingenuity, revolutionizing a number of industries with lifelike outputs like music and images. Misinformation and intellectual property rights give rise to ethical concerns. Notwithstanding these difficulties, generative AI has the potential to spur additional innovation in a variety of sectors, subject to moral issues. The goal of explainable AI (XAI) is to improve AI systems’ accountability and transparency, which is essential for their inclusion into industries like banking and healthcare. However, in order to effectively explain complicated AI systems, strong XAI techniques are required. Quantum artificial intelligence (QAI) promises improvements in cryptography and optimization by using quantum physics to speed up AI systems. However, there are still difficulties in creating practical quantum computers and improving quantum AI algorithms. The present overview provides new insights into developing a platform to explore AI in various arenas of life sciences and technologies and social up-gradation.

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Section: Applications Of Generative Ai In Data Science and Industry 50mentioning

confidence: 99%

Frontiers of Artificial Intelligence Versus Generative, Explainable and Quantum Technologies: An Overview

2024

OAJDA

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“…In this paper, we present secure two-party decision tree classifcation for diferent data sources' training and inference. Te two-party setting is reasonable for real-world applications [22] and has been widely employed in privacypreserving machine learning [23][24][25][26]. First, exploiting an advanced cryptographic primitive, function secret sharing (FSS) [27], we present an efcient comparison protocol for the choice of the best split.…”

Section: Overview Of Our Constructionmentioning

confidence: 99%

Secure Two-Party Decision Tree Classification Based on Function Secret Sharing

Liu,

Tang

2023

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Decision tree models are widely used for classification tasks in data mining. However, privacy becomes a significant concern when training data contain sensitive information from different parties. This paper proposes a novel framework for secure two-party decision tree classification that enables collaborative training and evaluation without leaking sensitive data. The critical techniques employed include homomorphic encryption, function secret sharing (FSS), and a custom secure comparison protocol. Homomorphic encryption allows computations on ciphertexts, enabling parties to evaluate an encrypted decision tree model jointly. FSS splits functions into secret shares to hide sensitive intermediate values. The comparison protocol leverages FSS to securely compare attribute values to node thresholds for tree traversal, reducing overhead through efficient cryptographic techniques. Our framework divides computation between two servers holding private data. A privacy-preserving protocol lets them jointly construct a decision tree classifier without revealing their respective inputs. The servers encrypt their data and exchange function secret shares to traverse the tree and obtain the classification result. Rigorous security proofs demonstrate that the protocol protects data confidentiality in a semihonest model. Experiments on benchmark datasets confirm that the approach achieves high accuracy with reasonable computation and communication costs. The techniques minimize accuracy loss and latency compared to prior protocols. Overall, the paper delivers an efficient, modular framework for practical two-party secure decision tree evaluation that advances the capability of privacy-preserving machine learning.

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MalDeXA - A Malware Detection system using XGBoost on Amazon Web Services

Cited by 2 publications

References 22 publications

Frontiers of Artificial Intelligence Versus Generative, Explainable and Quantum Technologies: An Overview

Frontiers of Artificial Intelligence Versus Generative, Explainable and Quantum Technologies: An Overview

Secure Two-Party Decision Tree Classification Based on Function Secret Sharing

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