3 citations · 7 across the 6 of their papers we have counts for
9 papers
Differential Privacy By Sampling
Josh Joy, Mario Gerla
In this paper we present the Sampling Privacy mechanism for privately releasing personal data. Sampling Privacy is a sampling based privacy mechanism that satisfies differential pr…
Distributed Differential Privacy By Sampling
Joshua Joy
In this paper, we describe our approach to achieve distributed differential privacy by sampling alone. Our mechanism works in the semi-honest setting (honest-but-curious whereby ag…
MPC Validation and Aggregation of Unit Vectors
Dylan Gray, Joshua Joy, Mario Gerla
When dealing with privatized data, it is important to be able to protect against malformed user inputs. This becomes difficult in MPC systems as each server should not contain enou…
Mobile Privacy-Preserving Crowdsourced Data Collection in the Smart City
Joshua Joy, Ciaran McGoldrick, Mario Gerla
Smart cities rely on dynamic and real-time data to enable smart urban applications such as intelligent transport and epidemics detection. However, the streaming of big data from Io…
LocationSafe: Granular Location Privacy for IoT Devices
Joshua Joy, Minh Le, Mario Gerla
Today, mobile data owners lack consent and control over the release and utilization of their location data. Third party applications continuously process and access location data w…
PAS-MC: Privacy-preserving Analytics Stream for the Mobile Cloud
Josh Joy, Mario Gerla
In today's digital world, personal data is being continuously collected and analyzed without data owners' consent and choice. As data owners constantly generate data on their perso…