Dynamic Key Generation for Polymorphic Encryption

Booher, Duane; Cambou, Bertrand; Carlson, Albert; Philabaum, Christopher

doi:10.1109/ccwc.2019.8666500

Cited by 8 publications

(3 citation statements)

References 6 publications

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“…Therefore, it is necessary to strengthen the robustness of aggregation algorithms against attacks, especially inference and backdoor attacks, to prevent malicious entities from reflecting exchanged messages, whether in the form of model updates, gradients, or parameters, in an effort to uncover the data used for local training. In this regard, several technologies can be explored, such as polymorphic encryption [92] and quantum-resistant cryptography [93].…”

Section: Improving Security and Privacymentioning

confidence: 99%

“…Despite the fact that the developed aggregation algorithms have found robust solutions to poisoning attacks such as the Byzantine attack [91], inference and backdoor attacks are still observed in this area, which requires further development and research. In addition, some techniques and methods in the aggregation algorithm domain are still unknown, such as the polymorphic encryption, "PE", which has proven to be a viable technology for exchanging encrypted data with high confidence in privacy, as explained in [92].…”

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confidence: 99%

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Reviewing Federated Learning Aggregation Algorithms; Strategies, Contributions, Limitations and Future Perspectives

et al. 2023

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The success of machine learning (ML) techniques in the formerly difficult areas of data analysis and pattern extraction has led to their widespread incorporation into various aspects of human life. This success is due in part to the increasing computational power of computers and in part to the improved ability of ML algorithms to process large amounts of data in various forms. Despite these improvements, certain issues, such as privacy, continue to hinder the development of this field. In this context, a privacy-preserving, distributed, and collaborative machine learning technique called federated learning (FL) has emerged. The core idea of this technique is that, unlike traditional machine learning, user data is not collected on a central server. Nevertheless, models are sent to clients to be trained locally, and then only the models themselves, without associated data, are sent back to the server to combine the different locally trained models into a single global model. In this respect, the aggregation algorithms play a crucial role in the federated learning process, as they are responsible for integrating the knowledge of the participating clients, by integrating the locally trained models to train a global one. To this end, this paper explores and investigates several federated learning aggregation strategies and algorithms. At the beginning, a brief summary of federated learning is given so that the context of an aggregation algorithm within a FL system can be understood. This is followed by an explanation of aggregation strategies and a discussion of current aggregation algorithms implementations, highlighting the unique value that each brings to the knowledge. Finally, limitations and possible future directions are described to help future researchers determine the best place to begin their own investigations.

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Section: Improving Security and Privacymentioning

confidence: 99%

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confidence: 99%

Reviewing Federated Learning Aggregation Algorithms; Strategies, Contributions, Limitations and Future Perspectives

et al. 2023

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“…PUF targets the nanoscale device parametric variations to create unclonable measurements of physical objects. Their ability to generate and store secret information makes them a good candidate for security systems [16,17]. PUFs generate challenge and response pairs (CRPs); the challenge is created during the enrollment, whereas the response is generated each time authen-tications are needed.…”

Section: Toolsmentioning

confidence: 99%

Statistical Analysis of ReRAM-PUF based Keyless Encryption Protocol Against Frequency Analysis Attack

Miandoab¹,

Assiri²,

Mihaljevic³

et al. 2021

Preprint

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There has been a growing interest in fully integrating Physical Unclonable Function (PUF) for cryptographic primitives, or keyless encryption. Keyless primitives do not store key information during the entire encryption and decryption phase, providing full security against volatile and non-volatile memory attacks. The concept of keyless encryption using ReRAM PUF is a relatively new concept, and the security aspect of the protocol has not been tested yet. In this paper, we use statistical models to analyze the randomness of the protocol and its resistance against frequency attacks.

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