MC-GEN: Multi-level clustering for private synthetic data generation

Li, Mingchen; Zhuang, Di; Chang, J. Morris

doi:10.1016/j.knosys.2022.110239

Cited by 5 publications

(1 citation statement)

References 10 publications

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“…Some researchers have developed large AI models by connecting millions of application programming interfaces (APIs) to enable rapid data access and to enhance the generalizability of AI models on unseen data [9]. In some cases, AI technologies were developed to curate data that are very close to real data to compensate for the lack of good data, or to train AI models with more data [10,11]. All these developments have mostly contributed to improving the accuracy of AI models, lowering the complications in terms of parameters/FLOPs (floating point operations), and extending the horizons of AI applications.…”

Section: Introductionmentioning

confidence: 99%

A Data-Centric AI Paradigm for Socio-Industrial and Global Challenges

Majeed,

Hwang

2024

Electronics

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Due to huge investments by both the public and private sectors, artificial intelligence (AI) has made tremendous progress in solving multiple real-world problems such as disease diagnosis, chatbot misbehavior, and crime control. However, the large-scale development and widespread adoption of AI have been hindered by the model-centric mindset that only focuses on improving the code/architecture of AI models (e.g., tweaking the network architecture, shrinking model size, tuning hyper-parameters, etc.). Generally, AI encompasses a model (or code) that solves a given problem by extracting salient features from underlying data. However, when the AI model yields a low performance, developers iteratively improve the code/algorithm without paying due attention to other aspects such as data. This model-centric AI (MC-AI) approach is limited to only those few businesses/applications (language models, text analysis, etc.) where big data readily exists, and it cannot offer a feasible solution when good data are not available. However, in many real-world cases, giant datasets either do not exist or cannot be curated. Therefore, the AI community is searching for appropriate solutions to compensate for the lack of giant datasets without compromising model performance. In this context, we need a data-centric AI (DC-AI) approach in order to solve the problems faced by the conventional MC-AI approach, and to enhance the applicability of AI technology to domains where data are limited. From this perspective, we analyze and compare MC-AI and DC-AI, and highlight their working mechanisms. Then, we describe the crucial problems (social, performance, drift, affordance, etc.) of the conventional MC-AI approach, and identify opportunities to solve those crucial problems with DC-AI. We also provide details concerning the development of the DC-AI approach, and discuss many techniques that are vital in bringing DC-AI from theory to practice. Finally, we highlight enabling technologies that can contribute to realizing DC-AI, and discuss various noteworthy use cases where DC-AI is more suitable than MC-AI. Through this analysis, we intend to open up a new direction in AI technology to solve global problems (e.g., climate change, supply chain disruption) that are threatening human well-being around the globe.

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Section: Introductionmentioning

confidence: 99%

A Data-Centric AI Paradigm for Socio-Industrial and Global Challenges

Majeed,

Hwang

2024

Electronics

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A High-Dimensional Data Trust Publishing Method Based on Attention Mechanism and Differential Privacy

Li,

Zhang,

Qian

et al. 2024

Lecture Notes in Computer Science

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Systematic Review of Generative Modelling Tools and Utility Metrics for Fully Synthetic Tabular Data

Lautrup,

Hyrup,

Zimek

et al. 2024

ACM Comput. Surv.

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Sharing data with third parties is essential for advancing science, but it is becoming more and more difficult with the rise of data protection regulations, ethical restrictions, and growing fear of misuse. Fully synthetic data, which transcends anonymisation, may be the key to unlocking valuable untapped insights stored away in secured data vaults. This review examines current synthetic data generation methods and their utility measurement. We found that more traditional generative models such as Classification and Regression Tree models alongside Bayesian Networks remain highly relevant and are still capable of surpassing deep learning alternatives like Generative Adversarial Networks. However, our findings also display the same lack of agreement on metrics for evaluation, uncovered in earlier reviews, posing a persistent obstacle to advancing the field. We propose a tool for evaluating the utility of synthetic data and illustrate how it can be applied to three synthetic data generation models. By streamlining evaluation and promoting agreement on metrics, researchers can explore novel methods and generate compelling results that will convince data curators and lawmakers to embrace synthetic data. Our review emphasises the potential of synthetic data and highlights the need for greater collaboration and standardisation to unlock its full potential.

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MC-GEN: Multi-level clustering for private synthetic data generation

Cited by 5 publications

References 10 publications

A Data-Centric AI Paradigm for Socio-Industrial and Global Challenges

A Data-Centric AI Paradigm for Socio-Industrial and Global Challenges

A High-Dimensional Data Trust Publishing Method Based on Attention Mechanism and Differential Privacy

Systematic Review of Generative Modelling Tools and Utility Metrics for Fully Synthetic Tabular Data

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