Deep Learning: Differential Privacy Preservation in the Era of Big Data

Vasa, Jalpesh; Thakkar, Amit

doi:10.1080/08874417.2022.2089775

Cited by 12 publications

(8 citation statements)

References 66 publications

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“…The model's performance, indicated by high accuracy, precision, recall, and F-score metrics, demonstrates the efficacy of machine learning algorithms in interpreting complex physiological data [48]. The segmentation of stroke lesions using the UNet model [49] further illustrates the capability of deep learning methods in medical imaging analysis, providing clear delineation of affected areas for accurate diagnosis [50].…”

Section: A Interpretation Of Resultsmentioning

confidence: 99%

Framework for Organization of Medical Processes in Medical Institutions Based on Big Data Technologies

Zhussipbek,

Turymbetov,

Ibragimova

et al. 2024

IJACSA

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This research paper delves into the burgeoning field of Big Data analytics in healthcare, proposing an innovative framework aimed at refining the organization and management of medical processes within healthcare institutions. Through the lens of detailed case studies, including stroke diagnosis leveraging the UNet model, and the identification of heart and respiratory diseases via machine learning algorithms applied to data from wearable devices, the study illuminates the profound capabilities of Big Data technologies in enhancing the precision of diagnostics, tailoring patient treatment, and elevating the overall efficiency of healthcare services. It meticulously interprets the outcomes of these applications, discusses the practical implications for healthcare professionals and institutions, confronts the challenges inherent in the integration of sophisticated analytics in clinical settings, and outlines potential directions for future research. Among the pivotal challenges highlighted are issues related to data privacy, security, the need for advanced infrastructure, and the imperative for ongoing training and interdisciplinary cooperation to navigate the complexities of Big Data in healthcare. The paper underscores the transformative promise of Big Data analytics, suggesting that comprehensive adoption and adept implementation could revolutionize healthcare delivery, making it more personalized, efficient, and cost-effective. Through this exploration, the paper contributes to the ongoing discourse on the integration of technology in healthcare, offering insights into how Big Data analytics can serve as a cornerstone for the next generation of medical diagnostics and patient care management, thereby enhancing health outcomes on a global scale.

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Section: A Interpretation Of Resultsmentioning

confidence: 99%

Framework for Organization of Medical Processes in Medical Institutions Based on Big Data Technologies

Zhussipbek,

Turymbetov,

Ibragimova

et al. 2024

IJACSA

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“…Algorithm 1 gives an overview the approach in this centralized setting. Below we give details for each of the steps in Algorithm 1 except for the last one, which simply runs a learning algorithm over the provided differentially private augmented data to obtain a classifier 9 .…”

Section: B Approach For the Centralized Settingmentioning

confidence: 99%

“…Privacy Enhancing Technologies (PETs) can be a potential solution to this conundrum, and the national strategy to advance privacy-preserving data sharing and analytics recognizes that PETs can protect privacy by removing personal information, by minimizing or reducing personal data, or by preventing undesirable processing of data, while maintaining the functionality of a system 3 . However, despite the development of advanced PETs such as secure multiparty computation [1], homomorphic encryption [2], differential privacy [3], zero knowledge proofs [4], synthetic data [5], federated learning [6], and trusted execution environments [7], as well as significant development of research papers applying them to solve problems [8], [9], their practical use is still quite limited.…”

Section: Introductionmentioning

confidence: 99%

U.S.-U.K. PETs Prize Challenge: Anomaly Detection via Privacy-Enhanced Federated Learning

Asif,

Min,

Wang

et al. 2024

IEEE Trans. Privacy

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Privacy Enhancing Technologies (PETs) have the potential to enable collaborative analytics without compromising privacy. This is extremely important for collaborative analytics can allow us to really extract value from the large amounts of data that are collected in domains such as healthcare, finance, and national security, among others. In order to foster innovation and move PETs from the research labs to actual deployment, the U.S. and U.K. governments partnered together in 2021 to propose the PETs prize challenge asking for privacy-enhancing solutions for two of the biggest problems facing us today: financial crime prevention and pandemic response. This article presents the Rutgers ScarletPets privacy-preserving federated learning approach to identify anomalous financial transactions in a payment network system (PNS). This approach utilizes a two-step anomaly detection methodology to solve the problem. In the first step, features are mined based on accountlevel data and labels, and then a privacy-preserving encoding scheme is used to augment these features to the data held by the PNS. In the second step, the PNS learns a highly accurate classifier from the augmented data. Our proposed approach has two major advantages: 1) there is no noteworthy drop in accuracy between the federated and the centralized setting, and 2) our approach is flexible since the PNS can keep improving its model and features to build a better classifier without imposing any additional computational or privacy burden on the banks. Notably, our solution won the first prize in the US for its privacy, utility, efficiency, and flexibility.

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“…Anonymization techniques can be used in the publication of data sets for research purposes, enabling information sharing without compromising individual privacy. However, challenges remain in achieving an optimal balance between privacy preservation and data utility [29]- [33].…”

Section: Practical Implementations and Case Studiesmentioning

confidence: 99%

Data Encryption and Anonymization Techniques for Enhanced Information System Security and Privacy

Pratomo,

Mokodenseho,

Aziz

2023

WSIST

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The rapid evolution of digital technology has ushered in a new era of data-driven information systems, bringing both unprecedented convenience and complex challenges to the forefront. This research delves into the realm of data encryption and anonymization techniques to enhance the security and privacy of information systems. The study encompasses a comprehensive exploration of diverse encryption and anonymization methods, evaluating their effectiveness through qualitative and quantitative analysis. A bibliometric analysis, facilitated by VOSviewer, unveils influential authors, research trends, and collaborative networks in the field. The research sheds light on the practical implications of encryption and anonymization techniques through real-world case studies and offers insights into the multidimensional landscape of information system security and user privacy.

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Deep Learning: Differential Privacy Preservation in the Era of Big Data

Cited by 12 publications

References 66 publications

Framework for Organization of Medical Processes in Medical Institutions Based on Big Data Technologies

Framework for Organization of Medical Processes in Medical Institutions Based on Big Data Technologies

U.S.-U.K. PETs Prize Challenge: Anomaly Detection via Privacy-Enhanced Federated Learning

Data Encryption and Anonymization Techniques for Enhanced Information System Security and Privacy

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