Data Integration and Privacy Systems Lab
Welcome to DIPS Lab
The Data Integration and Privacy Systems Lab (DIPS) is a cutting-edge research group at the University of Southampton, focusing on developing innovative solutions for secure data sharing, privacy-preserving integration, and intelligent policy management systems.
Our interdisciplinary team works on challenging problems at the intersection of data integration, privacy systems, automated negotiation, and trust management. We develop practical tools and theoretical frameworks that enable secure, efficient, and trustworthy data sharing across organizations.
The DIPS lab is part of the Web and Internet Science (WAIS) research group of the School of Electronics and Computer Science. WAIS is working on the intersection of Web, Data, AI and Society.
contact@dips.soton.ac.uk
Team
Head of Lab
Postdoctoral Researchers
Dr. Paolo Pareti
Senior Research Fellow
Dr. Soulmaz Gheisari
Research Fellow
Dr. Gheisari focuses on secure data sharing and privacy-preserving data analysis.
Scholar
Dr. Adeel Aslam
Research Fellow
Dr. Aslamβs research includes AI-driven policy systems and federated data access.
Scholar
π¨βπ
Dr. Jaime Salas
Research Fellow
Dr. Salas contributes to negotiation mechanisms and blockchain-based contracts.
Scholar
Dr. Bijay Prasad Jaysawal
Research Fellow
Dr. Miao Hu
Research Fellow
Miao's research includes federated learning and secure multiparty computation.
Scholar
Dr. George Giamouridis
Research Fellow
π¨βπ
Dr. Christopher Maidens
Research Fellow
Dr. Maidens' research focuses on data privacy and security.
PhD Students
π©βπ
Sam Richardson
PhD Researcher
π¨βπ
Wexuan Huang
PhD Researcher
π¨βπ
Xinzhuo Li
PhD Researcher
Past Members
Publications
Bag Containment of Join-on-Free Queries
Proceedings of the 28th International Conference on Database Theory (ICDT 2025).
2025
Data Sharing Negotiation and Contracting
Proceedings of the 23rd International Semantic Web Conference (2024).
2024
Towards modular data marketplaces
RuleML+RRβ24: Companion Proceedings of the 8th International Joint Conference on Rules and Reasoning (2024).
2024
Graph theory for consent management: A new approach for complex data flows
SIGMOD Record, 53(1), 55β63.
2024
Consent management in data workflows: A graph problem
Proceedings of the 26th International Conference on Extending Database Technology (EDBT) (2023). ACM SIGMOD Research Highlights Award.
2023
Selling decentralized knowledge graphs
Trusting Decentralised Knowledge Graphs and Web Data Workshop, European Semantic Web Conference, Hersonissos, Greece (2023).
2023
Data Marketplaces in the AI Economy
Symposium on AI, Data and Digitalization (SAIDD 2023) (2023).
2023
ForBackBench: From Database to Semantic Web mappings and back
Proceedings of the 22nd International Semantic Web Conference (2023).
2023
A Dual-Layer Privacy-Preserving Federated Learning Framework
Web Information Systems Engineering β WISE 2023, Lecture Notes in Computer Science, vol 14306. Springer, Singapore (2023).
2023
Satisfiability and containment of recursive SHACL
Journal of Web Semantics, 74, 100721.
2022
ForBackBench: A benchmark for chasing vs. query rewriting
Proceedings of the VLDB Endowment, 15(8), 1519-1532.
2022
OBDA vs forward chaining: The ForBackBench framework
Proceedings of the 21st International Semantic Web Conference (2022). Best Demo Award.
2022
Federated learning-based IoT intrusion detection on non-IID data
2022 Global Internet of Things Summit (GIoTS) (2022).
2022
A review of SHACL: From data validation to schema reasoning for RDF graphs
Reasoning Web International Summer School, 115-144 (2022).
2022
Enabling personal consent in databases
Proceedings of the VLDB Endowment, 15(2), 375-387.
2021
Social science for natural language processing: A hostile narrative analysis prototype
Proceedings of the 13th ACM Web Science Conference 2021, 102-111 (2021). Honourable mention paper.
2021
The need for machine-processable agreements in health data management
Algorithms, 13(87).
2020
Dataset search: A survey
The VLDB Journal, 29(1), 215-272.
2020
SHACL satisfiability and containment
Proceedings of the 19th International Semantic Web Conference, 474-493. Springer, Cham (2020).
2020
Rule applicability on RDF triplestore schemas
Workshop on AI for Internet of Things at the 29th International Joint Conference on Artificial Intelligence and the 17th Pacific Rim International Conference on Artificial Intelligence (IJCAI-PRICAI 2020), Yokohama, Japan.
2020
The secret life of immortal data
In Proceedings of the 12th ACM Conference on Web Science Companion (pp. 89β90).
2020
Foundations of ontology-based data access under bag semantics
Artificial Intelligence, 274, 91-132.
2019
A policy editor for semantic sensor networks
Proceedings of the 18th International Semantic Web Conference (ISWC 2019), Auckland, New Zealand, October 26β30.
2019
SHACL constraints with inference rules
Proceedings of the 18th International Semantic Web Conference (ISWC 2019). Lecture Notes in Computer Science, vol. 11778. Springer, Cham.
2019
Towards an ontology for public procurement based on the open contracting data standard
Conference on e-Business, e-Services and e-Society, 230-237. Springer, Cham.
2019
Attacking Diophantus: Solving a special case of bag containment
Proceedings of the 38th ACM SIGMOD-SIGACT-SIGAI Symposium on Principles of Database Systems (PODS 2019).
2019
The need for data sharing agreements in data management
Semantic Web Technologies for Health Data Management Workshop, Proceedings of the 18th International Semantic Web Conference (pp. 5β8).
2019
more publications in each team member's profile
Our Tools
Policy Engine
Create, manage, update, store, evaluate, enforce and compare policies for data sharing, access and usage.
Negotiation & Contracts
Automated negotiation of data sharing contracts. Negotiate on the price, terms, policies and text of an agreement. Sign, manage and enforce contracts.
Trust and Reputation Management
Evaluate the trustworthiness of data sharing partners. Maintain, create and revoke endorsements of trust and reputation.
Consent Management
Release data requests, manage user consent, revoke, update, and delete consent policies.
Projects
Active
UPCAST Project provides a set of universal, trustworthy, transparent, and user-friendly data market plugins for the automation of data sharing and processing agreements between businesses, public administrations and citizens. Our plugins will enable actors in the common European data spaces to design and deploy data exchange and trading operations guaranteeing Automatic negotiation of agreement terms, Dynamic fair pricing, Improved data-asset Discovery, Privacy, commercial and administrative confidentiality requirements, Low environmental footprint, Relevant legislation, Ethical and responsibility guidelines.
Active
DataPACT focuses on revolutionizing data/AI operations by integrating compliance, privacy, and environmental sustainability into their core design. This involves developing innovative technical tools (Compliance Toolbox) and tool-supported methodologies (Compliance Framework) for compliance assessment and realization of data/AI pipelines designed, deployed, and executed through a set of pipeline management tools and techniques (Compliance-aware Data/AI Pipeline Toolbox). These tools will simplify regulatory adherence and help organizations create fair, privacy-respecting, and environmentally conscious systems. DataPACT also supports the development of open European data spaces, enabling seamless and trusted data exchange across borders.
Active
RAISE project aims to provide the mechanisms for a distributed crowdsourced data processing system, moving from open data to data open for processing. To do so, RAISE will attempt to adapt open data to the culture of the research community, ensuring FAIR principles. The vision of the project is the EOSC Web of FAIR Data and Services for Science is an open, fair and reliable Research Community where every researcher will be accredited for their work and all research data will be equally accessible for processing without violating data protection regulations.