Anonymization as homeomorphic data space transformation for privacy-preserving deep learning

Girka, Anastasiia; Terziyan, Vagan; Gavriushenko, Mariia; Gontarenko, Andrii

doi:10.1016/j.procs.2021.01.337

Cited by 9 publications

(3 citation statements)

References 19 publications

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“…As mentioned above, a promising solution for privacy preservation is homomorphic encryption, but there are also other techniques for data encryption and/or anonymization. For example, on the basis of homomorphic data space transformation, Girka et al [38] proposed an anonymization algorithm to protect data, while still allowing neural network training. They analyzed the effects that this method has on the neural network perfor-mance.…”

Section: Review Of Privacy-preserving Ai In Industrial Applicationsmentioning

confidence: 99%

Privacy-Preserving and Explainable AI in Industrial Applications

et al. 2022

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The industrial environment has gone through the fourth revolution, also called “Industry 4.0”, where the main aspect is digitalization. Each device employed in an industrial process is connected to a network called the industrial Internet of things (IIOT). With IIOT manufacturers being capable of tracking every device, it has become easier to prevent or quickly solve failures. Specifically, the large amount of available data has allowed the use of artificial intelligence (AI) algorithms to improve industrial applications in many ways (e.g., failure detection, process optimization, and abnormality detection). Although data are abundant, their access has raised problems due to privacy concerns of manufacturers. Censoring sensitive information is not a desired approach because it negatively impacts the AI performance. To increase trust, there is also the need to understand how AI algorithms make choices, i.e., to no longer regard them as black boxes. This paper focuses on recent advancements related to the challenges mentioned above, discusses the industrial impact of proposed solutions, and identifies challenges for future research. It also presents examples related to privacy-preserving and explainable AI solutions, and comments on the interaction between the identified challenges in the conclusions.

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Section: Review Of Privacy-preserving Ai In Industrial Applicationsmentioning

confidence: 99%

Privacy-Preserving and Explainable AI in Industrial Applications

et al. 2022

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“…In [13], the new algorithm has been suggested for protecting sensitive data (against adversarial attacks) used for training and testing intelligent systems based on deep neural networks. It can be used as an important feature of the digital immune system and as a complementary alternative to a digital vaccination concept.…”

Section: Immune Project Puzzlesmentioning

confidence: 99%

Hybrid Threats against Industry 4.0: Adversarial Training of Resilience

Kaikova

Terziyan²,

Tiihonen³

et al. 2022

E3S Web Conf.

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Industry 4.0 and Smart Manufacturing are associated with the Cyber-Physical-Social Systems populated and controlled by the Collective Intelligence (human and artificial). They are an important component of Critical Infrastructure and they are essential for the functioning of a society and economy. Hybrid Threats nowadays target critical infrastructure and particularly vulnerabilities associated with both human and artificial intelligence. This article summarizes some latest studies of WARN: “Academic Response to Hybrid Threats” (the Erasmus+ project), which aim for the resilience (regarding hybrid threats) of various Industry 4.0 architectures and, especially, of the human and artificial decision-making within Industry 4.0 processes. This study discovered certain analogy between (cognitive) resilience of human and artificial intelligence against cognitive hacks (special adversarial hybrid activity) and suggested the approaches to train the resilience with the special adversarial training techniques. The study also provides the recommendations for higher education institutions on adding such training and related courses to their various programs. The specifics of related courses would be as follows: their learning objectives and related intended learning outcomes are not an update of personal knowledge, skills, beliefs or values (traditional outcomes) but the robustness and resilience of the already available ones.

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“…Sensitive information can be more difficult to distinguish from non-sensitive information thanks to privacy-preserving measures that aim to prevent the leakage of sensitive information. But they don't rule out the possibility of finding inference laws [10] [11]. Therefore, in recent years, experts have concentrated their attention on association norms that protect personal information.…”

Section: Introductionmentioning

confidence: 99%

A Review on Privacy Preserving Using Machine learning and Deep Learning Techniques

Rajput¹,

Tiwari²

2023

IJRASET

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In order to entice data custodians to provide precise documentation so that data mining can continue with confidence, protecting the confidentiality of healthcare data is crucial. Association rule mining has been extensively used in the past to analyze healthcare data. The majority of applications ignore the drawbacks of specific diagnostic procedures in favor of positive association criteria. Negative association criteria may provide more useful information when bridging disparate diseases and medications than positive ones. In the case of doctors and social groups, this is particularly accurate. Data mining for medical purposes must be done with patient identities protected, especially when working with sensitive data. However, it might be attacked if this information becomes public. In order to perform data mining research, technology that modifies data (data sanitization) that reconstructs aggregate distributions has recently addressed the importance of healthcare data privacy. This study examines data sanitization in healthcare data mining using metaheuristics in order to safeguard patient privacy. Studies on SHM have looked at the uses of IoT &/or Machine Learning (ML) within the field, as well as the architecture, security, & privacy issues. However, no studies have looked into how AI and ubiquity computing technologies have affected SHM systems. The objective of this research is to identify and map the primary technical concepts within the SHM framework.

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Anonymization as homeomorphic data space transformation for privacy-preserving deep learning

Cited by 9 publications

References 19 publications

Privacy-Preserving and Explainable AI in Industrial Applications

Privacy-Preserving and Explainable AI in Industrial Applications

Hybrid Threats against Industry 4.0: Adversarial Training of Resilience

A Review on Privacy Preserving Using Machine learning and Deep Learning Techniques

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