Visible to the public A Framework for Supporting Privacy Preservation Functions in a Mobile Cloud Environment

TitleA Framework for Supporting Privacy Preservation Functions in a Mobile Cloud Environment
Publication TypeConference Paper
Year of Publication2022
AuthorsTomaras, Dimitrios, Tsenos, Michail, Kalogeraki, Vana
Conference Name2022 23rd IEEE International Conference on Mobile Data Management (MDM)
KeywordsAnalytical models, Costs, Data models, data privacy, expert systems, Human Behavior, mobile cloud, privacy, pubcrawl, resilience, Resiliency, Scalability, serverless, Serverless Computing, Throughput
AbstractThe problem of privacy protection of trajectory data has received increasing attention in recent years with the significant grow in the volume of users that contribute trajectory data with rich user information. This creates serious privacy concerns as exposing an individual's privacy information may result in attacks threatening the user's safety. In this demonstration we present TP$^\textrm3$ a novel practical framework for supporting trajectory privacy preservation in Mobile Cloud Environments (MCEs). In TP$^\textrm3$, non-expert users submit their trajectories and the system is responsible to determine their privacy exposure before sharing them to data analysts in return for various benefits, e.g. better recommendations. TP$^\textrm3$ makes a number of contributions: (a) It evaluates the privacy exposure of the users utilizing various privacy operations, (b) it is latency-efficient as it implements the privacy operations as serverless functions which can scale automatically to serve an increasing number of users with low latency, and (c) it is practical and cost-efficient as it exploits the serverless model to adapt to the demands of the users with low operational costs for the service provider. Finally, TP$^\textrm3$'s Web-UI provides insights to the service provider regarding the performance and the respective revenue from the service usage, while enabling the user to submit the trajectories with recommended preferences of privacy.
DOI10.1109/MDM55031.2022.00061
Citation Keytomaras_framework_2022