2 citations · 2 across the 7 of their papers we have counts for
6 papers · 1 filter
Towards Inference-Aware Privacy Guidance for Data Preparation
Vishal Chakraborty, Felix Naumann
Data preparation often begins with sensitive data and produces a releasable artifact for analysis, sharing, or model training. Existing workflows are primarily guided by utility: a…
Don't Stir the Pot! Authorized Vector Data Retrieval via Access-Aware Indexing
Shanshan Han, Vishal Chakraborty, Sharad Mehrotra
Vector databases increasingly enforce role-based access control, where each top-k approximate nearest neighbor query must return only vectors the querying role is authorized to acc…
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…
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…
Hiding Access-pattern is Not Enough! Veil: A Storage and Communication Efficient Volume-Hiding Algorithm
Shanshan Han, Vishal Chakraborty, Michael Goodrich +2
This paper addresses volume leakage (i.e., leakage of the number of records in the answer set) when processing keyword queries in encrypted key-value (KV) datasets. Volume leakage,…
Data-CASE: Grounding Data Regulations for Compliant Data Processing Systems
Vishal Chakraborty, Stacy Ann-Elvy, Sharad Mehrotra +5
Data regulations, such as GDPR, are increasingly being adopted globally to protect against unsafe data management practices. Such regulations are, often ambiguous (with multiple va…