# A Usable Encryption Solution for File-Based Geospatial Data within a Database File System
## 1 Introduction  
Unavailability or compromise of spatial data can have serious consequences. GIS data are a key component of decision-making in high-risk scenarios such as humanitarian and natural disasters. Timely access to relevant spatial data is of the utmost importance under these conditions; however, it is also important to understand that although there is a great need for data to be highly available, keeping this data _secure_ is critical, as recently highlighted by researchers [1, 2, 3, 4, 5, 6]. Almost 60-80% of all generated data have some geographic component to them [7], and, adding to this, the complexities associated with spatial data have increased dramatically [8]. Due to the multiplicity of available spatial formats, files are often scattered on different servers, namely, file, web, email, and document management servers [9]. Spatial _\"data silos\"_ housing file data across multiple formats and the associated physical and virtual mediums reduce their utility and availability for critical decision-making scenarios [10, 11, 12]. These data silos also increase access points to sensitive or proprietary information. The cloud helps eliminate technological barriers to collaboration and offers a equipped solution for connecting siloed data. It also addresses several limitations of traditional storage systems--supporting unstructured data models, high concurrency, low latency,high flexibility, high scalability, and availability. Geospatial data are also increasingly stored in third-party services (such as those provided by _Google_, _Amazon_, and _Microsoft_) due to the cost and reliability of cloud solutions. Indeed, cloud-based geospatial data protection approaches now include encryption in their solutions. Esri's ArcGIS Online [13], Microsoft Azure Orbital's offering for geospatial data [14], and Google Bigquery [15] all offer solutions for geospatial data encryption at present. However, moving data storage infrastructures from on-premise to distributed cloud-based architectures increases security and privacy risks [16, 17, 18], and sensitive or valuable geospatial data must be safeguarded from companies seeking profit. A cloud service provider guarding valuable data or digital assets must be _trusted_. Yet, in the past four years, prominent companies like _Facebook_, _Alibaba_, and _LinkedIn_, as well as a GIS company, _PeopleGIS_, experienced cloud security breaches, exposing millions of records, the majority of which contained location information [19, 20]. While these breaches were from unknown adversaries, individuals within corporations can also gain unauthorized data access. Thus, spatial data security and the privacy of users in the cloud are still challenges [21], and the majority of organisations _do not_ store mission-critical data in the cloud, as they argue there is a higher degree of confidence in security when the data are stored _on-site_[22]. As such, protecting file-based spatial data requires _revisiting_ and _extending_ existing storage security technologies that can be leveraged on-site. Leading DBMS vendors (Oracle, IBM, Lotus, and Microsoft) came up with an innovative idea some years back--_creating a file system around a database_. This scheme of storing entire OS files in a database, which we call a _Database File System_ (DBFS), bundled with commonly used protocol services (for example, web, shared folders, or file transfer), opens up several possibilities for spatial files. First, files previously existing in various data silos around the GIS enterprise can be consolidated. Second, this scheme promises database features that until now have only benefited structured data, such as content management, centralised authentication and access control mechanisms, which are beyond the capabilities of standard file systems. Finally, it offers robust DBMS management features to spatial files, namely, _reliability, user management, performance, scalability,_ and _security_.