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Masoom Tulsiani

dblp:431/1862 · DBLP profile ↗
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1ranked-venue papers
0as first author
1since 2021 · last 2026
0009-0003-0031-0666ORCID · reported

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

Software engineering, systems software and programming languages · 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
1 paper
Privacy and data protection · 100%
Computer architecture, parallel and distributed computing, and storage systems
1 paper
Cloud and datacenter computing · 100%

Topics — the 3 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Privacy and data protection › differential privacy
synthetic data generation
1.012026
Addressing Data Scarcity with Synthetic Data: A Secure and GDPR-Compliant Cloud-Based Platform · ACM Trans. Softw. Eng. Methodol. 2026
Privacy and data protection › data publishing › privacy-preserving data publishing
synthetic data privacy
1.012026
Addressing Data Scarcity with Synthetic Data: A Secure and GDPR-Compliant Cloud-Based Platform · ACM Trans. Softw. Eng. Methodol. 2026
Cloud and datacenter computing
cloud platform
0.312026
Addressing Data Scarcity with Synthetic Data: A Secure and GDPR-Compliant Cloud-Based Platform · ACM Trans. Softw. Eng. Methodol. 2026
YearPublicationVenuePosition
2026 Addressing Data Scarcity with Synthetic Data: A Secure and GDPR-Compliant Cloud-Based Platform
abstract
This study presents a cloud-based platform for synthetic data generation, validation, and evaluation, developed to address data scarcity in the telecommunications sector while ensuring compliance with the General Data Protection Regulation (GDPR). In collaboration with a Dutch telecommunications provider facing data scarcity due to low user-consent rates, we developed a platform that allows synthetic data vendors to securely generate synthetic data based on schema input without accessing sensitive information. Vendors uploaded containerized executables for synthetic data generation and the platform automated infrastructure provisioning, ensuring no access to personal data. A validation mechanism minimized the risk of re-identification by ensuring that the synthetic data did not inadvertently replicate real data points. We mutually agreed with the vendors on five evaluation metrics and the platform logged and calculated performance for each, allowing them to refine their algorithms. To validate the platform’s performance, we conducted an offline study with the TV viewership team, using each vendor’s synthetic data to generate viewership categories. The vendor with the best evaluation metrics also produced categories most similar to the real data, confirming the platform’s effectiveness. This study, involving two vendors and a telecommunications company, demonstrated the platform’s applicability in addressing business challenges while ensuring privacy compliance.
Nemania Borovits, Gianluigi Bardelloni, Hossein Hashemi 0005, Masoom Tulsiani, Damian A. Tamburri, Willem-Jan van den Heuvel
ACM Trans. Softw. Eng. Methodol.4