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Shripad Gade

dblp:185/0652 · DBLP profile ↗
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2ranked-venue papers
0as first author
2since 2021 · last 2025
0000-0003-3400-749XORCID · corroborated

Domains — the database's venue-derived domains; a paper can count in several

Databases, data management, data science and information retrieval · 2 · 2 since 2021Artificial intelligence and machine learning · 1 · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Network and information security
2 papers
Privacy and data protection · 60% Security and privacy of machine learning · 20% Network security · 20%
Databases, data mining, and information retrieval
1 paper
Data integration and cleaning · 100%
Artificial intelligence
1 paper
Generative modeling · 100%

Topics — the 8 heaviest of 8, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Data integration and cleaning › data generation
synthetic data generation		 0.912025
Synthetic Tabular Data: methods, attacks and defenses · Proc. VLDB Endow. 2025
Data integration and cleaning › data generation › synthetic data generation
tabular data synthesis		 0.912025
Synthetic Tabular Data: methods, attacks and defenses · Proc. VLDB Endow. 2025
Network security › attack resilience
attack mitigation		 0.912025
Synthetic Tabular Data: methods, attacks and defenses · Proc. VLDB Endow. 2025
Security and privacy of machine learning
privacy attack		 0.912025
Synthetic Tabular Data: Methods, Attacks and Defenses · KDD (2) 2025
Privacy and data protection › differential privacy
synthetic data generation		 0.912025
Synthetic Tabular Data: Methods, Attacks and Defenses · KDD (2) 2025
Privacy and data protection › data publishing › privacy-preserving data publishing
synthetic data privacy		 0.912025
Synthetic Tabular Data: methods, attacks and defenses · Proc. VLDB Endow. 2025
Privacy and data protection › data publishing
tabular synthetic data		 0.912025
Synthetic Tabular Data: Methods, Attacks and Defenses · KDD (2) 2025
Machine learning › Generative modeling › diffusion model
tabular data generation		 0.312025
Synthetic Tabular Data: methods, attacks and defenses · Proc. VLDB Endow. 2025

Methods — techniques the papers use, named apart from their topics

deep learning · 3.5probabilistic graphical model · 1.7probabilistic graphical models · 1.7
YearPublicationVenuePosition
2025 Synthetic Tabular Data: Methods, Attacks and Defenses
abstract
Synthetic data is often positioned as a solution to replace sensitive fixed-size datasets with a source of unlimited matching data, freed from privacy concerns. There has been much progress in synthetic data generation over the last decade, leveraging corresponding advances in machine learning and data analytics. In this survey, we cover the key developments and the main concepts in tabular synthetic data generation, including paradigms based on probabilistic graphical models and on deep learning. We provide background and motivation, before giving a technical deep-dive into the methodologies. We also address the limitations of synthetic data, by studying attacks that seek to retrieve information about the original sensitive data. Finally, we present extensions and open problems in this area.
Graham Cormode, Samuel Maddock, Enayat Ullah, Shripad Gade
 KDD (2)4
2025 Synthetic Tabular Data: methods, attacks and defenses
abstract
Synthetic data is often positioned as a solution to replace sensitive fixed-size data sets with a source of unlimited matching data, freed from privacy concerns. There has been much progress in synthetic data generation over the last decade, leveraging corresponding advances in machine learning and data analytics. In this tutorial, we survey the key developments and the main concepts in tabular synthetic data generation, including paradigms based on probabilistic graphical models and on deep learning. We provide background and motivation, before giving a technical deep-dive into the methodologies. We also address the limitations of synthetic data, by studying attacks that seek to retrieve information about the original sensitive data. Finally, we present extensions and open problems in this area.
Graham Cormode, Shripad Gade, Samuel Maddock, Enayat Ullah
 Proc. VLDB Endow.2