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Prior work suggests that users conceptualize the organization of personal collections of digital files through the lens of similarity. However, it is unclear to what degree similar files are actually located near one another (e.g., in the same directory) in actual file collections, or whether leveraging file similarity can improve information retrieval and organization for disorganized collections of files. To this end, we conducted an online study combining automated analysis of 50 Google Drive and Dropbox users' cloud accounts with a survey asking about pairs of files from those accounts. We found that many files located in different parts of file hierarchies were similar in how they were perceived by participants, as well as in their algorithmically extractable features. Participants often wished to co-manage similar files (e.g., deleting one file implied deleting the other file) even if they were far apart in the file hierarchy. To further understand this relationship, we built regression models, finding several algorithmically extractable file features to be predictive of human perceptions of file similarity and desired file co-management. Our findings pave the way for leveraging file similarity to automatically recommend access, move, or delete operations based on users' prior interactions with similar files.

Navigating through a file hierarchy is one of the most common methods for accessing files, yet it can be slow and repetitive. New algorithms that predict upcoming file accesses have the potential to improve navigation-based file retrieval, but it is unknown how best to present their predictions to users. We present three design goals aiming to improve navigation-based file retrieval interfaces: minimise the time spent at each hierarchical level en route to the target file; reduce the number of levels traversed by providing shortcuts; and promote rehearsal of the retrieval mechanics to facilitate expertise. We introduce three interfaces that augment standard file browsers based on each of these goals: Icon Highlights give greater prominence to predicted items in the current folder; Hover Menus provide shortcuts to predicted folder content; and Search Directed Navigation uses predictive highlighting to guide users through the hierarchy in response to query terms. Results from a user evaluation show that all three interfaces improve file retrieval times, with Icon Highlights and Hover Menus best suited for frequently accessed items and Search Directed Navigation best suited for infrequent ones. We also show that the benefits are larger when folder content is spatially unstable. Finally, we discuss how the interfaces could be combined and deployed in existing file browsers.

One of the most fundamental issues in computer security is protecting sensitive files from unauthorized access. Traditional file protection tools run inside the target operating system, which hosts sensitive files. This makes previous approaches vulnerable in face of a compromised OS. To address this limitation, recent approaches seek for a good isolation by putting file system into a dedicated virtual machine or by using a network file system. However, they suffer a sharp increase in trusted computing base size which degrades their reliability.In this paper, we present Filesafe, a special purpose hy-pervisor aimed at protecting sensitive files in a compromised operating system. It bridges the semantic gap between guest OS and hypervisor by reconstructing file hierarchy from raw data, which incurs no runtime overhead. By enforcing security policies created in hypervisor, Filesafe could prevent sensitive files from unauthorized access even if they have kernel privileges in guest OS. We have implemented a proof-of-concept prototype on Windows XP with FAT32 file system. Furthermore, we evaluate Filesafe's performance and code size to demonstrate it is practical in real world scenarios.