2 citations · 3 across the 8 of their papers we have counts for
8 papers
ReBound: Reuse-Aware Privacy For Interactive Decision Support
Nada Lahjouji, Shufan Zhang, Xi He +1
Differentially private decision support frameworks answer complex aggregate threshold queries with formal bounds on false negative and false positive rates, but treat each query in…
Inference-Aware & Privacy-Preserving Deletion in Databases
Vishal Chakraborty, Youri Kaminsky, Arnav Abhijit Dhariya +3
Deletion is a fundamental database operation, yet modern systems often fail to provide the privacy guarantee that users expect from it. A deleted value may disappear from query res…
Scalable Enforcement of Fine Grained Access Control Policies in Relational Database Management Systems
Anadi Shakya, Primal Pappachan, David Maier +3
The proliferation of smart technologies and evolving privacy regulations such as the GDPR and CPRA has increased the need to manage fine-grained access control (FGAC) policies in d…
Meaningful Data Erasure in the Presence of Dependencies
Vishal Chakraborty, Youri Kaminsky, Sharad Mehrotra +5
Data regulations like GDPR require systems to support data erasure but leave the definition of "erasure" open to interpretation. This ambiguity makes compliance challenging, especi…
Access Control for Information-Theoretically Secure Key-Document Stores
Yin Li, Sharad Mehrota, Shantanu Sharma +1
This paper presents a novel key-based access control technique for secure outsourcing key-value stores where values correspond to documents that are indexed and accessed using keys…
DIM-SUM: Dynamic IMputation for Smart Utility Management
Ryan Hildebrant, Rahul Bhope, Sharad Mehrotra +2
Time series imputation models have traditionally been developed using complete datasets with artificial masking patterns to simulate missing values. However, in real-world infrastr…
SES: Bridging the Gap Between Explainability and Prediction of Graph Neural Networks
Zhenhua Huang, Kunhao Li, Shaojie Wang +3
Despite the Graph Neural Networks' (GNNs) proficiency in analyzing graph data, achieving high-accuracy and interpretable predictions remains challenging. Existing GNN interpreters…
Graph Structure Prompt Learning: A Novel Methodology to Improve Performance of Graph Neural Networks
Zhenhua Huang, Kunhao Li, Shaojie Wang +3
Graph neural networks (GNNs) are widely applied in graph data modeling. However, existing GNNs are often trained in a task-driven manner that fails to fully capture the intrinsic n…