StreetX: Spatio-Temporal Access Control Model for Data

TL;DR

StreetX is a scalable spatio-temporal access control model that allows users to specify complex space and time constraints on data sharing, automatically resolving conflicts and optimizing query performance.

Contribution

We introduce StreetX, a novel spatio-temporal access control framework that supports arbitrary space and time constraints and handles conflicts efficiently.

Findings

Successfully implemented on large datasets with 10 million records.

Supports complex region and time window constraints.

Improves query performance through conflict resolution and optimization.

Abstract

Cities are a big source of spatio-temporal data that is shared across entities to drive potential use cases. Many of the Spatio-temporal datasets are confidential and are selectively shared. To allow selective sharing, several access control models exist, however user cannot express arbitrary space and time constraints on data attributes using them. In this paper we focus on spatio-temporal access control model. We show that location and time attributes of data may decide its confidentiality via a motivating example and thus can affect user's access control policy. In this paper, we present StreetX which enables user to represent constraints on multiple arbitrary space regions and time windows using a simple abstract language. StreetX is scalable and is designed to handle large amount of spatio-temporal data from multiple users. Multiple space and time constraints can affect performance of the query and may also result in conflicts. StreetX automatically resolve conflicts and optimizes the query evaluation with access control to improve performance. We implemented and tested prototype of StreetX using space constraints by defining region having 1749 polygon coordinates on 10 million data records. Our testing shows that StreetX extends the current access control with spatio-temporal capabilities.