Title: Privacy-preserving publishing of Knowledge Graphs
Abstract
Data sharing is crucial in the era of big data, and protecting users' sensitive information in these data is as vital as analyzing them. Knowledge graphs (KGs) are increasingly pivotal in data sharing due to their flexibility in modeling both attributes' values and relationships. However, due to the rich information in shared KGs, users' privacy is easier to breach. Thus, data providers must anonymize their KGs before sharing them. Unfortunately, data providers cannot straightforwardly use anonymization techniques developed for relational and traditional graphs to anonymize KGs as they do not consider both users' attributes and their relationships simultaneously. In this talk, we present a framework for anonymizing KGs, targeting three scenarios of increasing complexity: static publishing, sequential publishing, and personalized publishing. The first scenario allows data providers to publish their anonymized KGs once. The second one extends the first to enable the providers to publish new anonymized versions of their KGs. The final one lets users specify their privacy protection levels and anonymize KGs to protect all users under their levels.
Biography
Elena Ferrari is a professor of Computer Science at the University of Insubria (Italy), where she leads the STRICT SociaLab. She received her Ph.D. and M.Sc. degrees in Computer Science from the University of Milano (Italy). Her research interests are in the broad area of cybersecurity, privacy, and trust. Current research includes security and privacy for IoT, privacy-preserving data publishing, machine learning for cybersecurity, and blockchain. She is a fellow member of ACM and IEEE. She has been the recipient of several prestigious awards, including the 2009 IEEE Technical Achievement Award for pioneering contributions to Secure Data Management, the 2021 ACM SIGSAC Outstanding Contributions Award, the ACM CODASPY Research Award, and the ACM SACMAT 10-Year Test of Time Award. She is the recipient of the 2024 IEEE Innovation in Societal Infrastructure Award for pioneering and sustained contributions to the security and privacy of online social networks. In 2018, she was named one of the 50 most influential Italian women in tech.
Title: Data Management for Deep Learning
Abstract
Deep learning (DL) has made significant progress and found wide application in various fields, like chaptGPT for question answering. However, the success and efficiency of DL models depend on proper data management. Training deep learning-based image classifiers is challenging without labeled data, and efficiency is hindered by large datasets, complex models, and numerous hyperparameters. Lack of validation and explanation limits model applicability. In this presentation, I will discuss three crucial issues in data management for deep learning: 1) effective data preparation for DL, including extraction, integration, and labeling; 2) DL training optimization, involving data compression and computation graph optimization; and 3) the importance of model explanation for robustness and transparency. I will conclude by highlighting future research directions.
Biography
Lei Chen, is a chair professor in the data science and analytic thrust at HKUST (GZ), Fellow of the IEEE, and a Distinguished Member of the ACM. Currently, Prof. Chen serves as the dean of information hub, the director of Big Data Institute at HKUST, MOE/MSRA Information Technology Key Laboratory. Prof. Chen’s research interests include Data-driven AI, knowledge graphs, blockchains, data privacy, crowdsourcing, spatial and temporal databases and query optimization on large graphs and probabilistic databases. He received his BS degree in computer science and engineering from Tianjin University, Tianjin, China, MA degree from Asian Institute of Technology, Bangkok, Thailand, and PhD in computer science from the University of Waterloo, Canada. Prof. Chen received the SIGMOD Test-of-Time Award in 2015, Best research paper award in VLDB 2022, .The system developed by Prof. Chen’s team won the excellent demonstration award in VLDB 2014. Prof. Chen had served as VLDB 2019 PC Co-chair. Currently, Prof. Chen serves as Editor-in-chief of IEEE Transaction on Data and Knowledge Engineering and an executive member of the VLDB endowment.
Title: Towards Practical, Scalable and Private Management of Cloud Data
Abstract
Due to the widespread use of cloud applications, searching for data from a cloud server has become ubiquitous. However, accessing data stored in a cloud server comes with severe privacy concerns owing to numerous attacks and data breaches. Much research has focused on preserving the privacy of data stored in the cloud using various advanced cryptographic techniques. Our goal in this talk is to demonstrate how private access of data can become a practical reality in the near future. Our focus is on supporting oblivious queries and thus hide any associated access patterns on both private and public data. For private data, ORAM (Oblivious RAM) is one of the most popular approaches for supporting oblivious access to encrypted data. However, most existing ORAM datastores are not fault tolerant and hence an application may lose all of its data when failures occur. To achieve fault tolerance, we propose QuORAM, the first datastore to provide oblivious access and fault-tolerant data storage using a quorum-based replication protocol. For public data, PIR (Private Information Retrieval) is the main mechanism proposed in recent years. However, PIR requires the server to consider data as an array of elements and clients retrieve data using an index into the array. This requirement limits the use of PIR in many practical settings, especially for key-value stores, where the client may be interested in a particular key, but does not know the exact location of the data at the server. In this talk we will discuss recent efforts to overcome these limitations, using Fully Homomorphic Encryption (FHE), to improve the performance, scalability and expressiveness of privacy preserving queries of public data.
Biography
Amr El Abbadi is a Professor of Computer Science. He received his B. Eng. from Alexandria University, Egypt, and his Ph.D. from Cornell University. His research interests are in the fields of fault-tolerant distributed systems and databases, focusing recently on Cloud data management, blockchain based systems and privacy concerns. Prof. El Abbadi is an ACM Fellow, AAAS Fellow, and IEEE Fellow. He was Chair of the Computer Science Department at UCSB from 2007 to 2011. He served as Associate Graduate Dean at the University of California, Santa Barbara from 2021--2023. He has served as a journal editor for several database journals, including, The VLDB Journal, IEEE Transactions on Computers and The Computer Journal. He has been Program Chair for multiple database and distributed systems conferences, including most recently SIGMOD 2022. He currently serves on the executive committee of the IEEE Technical Committee on Data Engineering (TCDE) and was a board member of the VLDB Endowment from 2002 to 2008. In 2007, Prof. El Abbadi received the UCSB Senate Outstanding Mentorship Award for his excellence in mentoring graduate students. In 2013, his student, Sudipto Das received the SIGMOD Jim Gray Doctoral Dissertation Award. Prof. El Abbadi is also a co-recipient of the Test of Time Award at EDBT/ICDT 2015. He has published over 350 articles in databases and distributed systems and has supervised over 40 PhD students.
