Creating information technologies intended for broad use that allow everyone to participate safely online—which we refer to as inclusive digital safety —requires understanding and addressing the digital-safety needs of a diverse range of users who face elevated risk of technology-facilitated attacks or disproportionate harm from such attacks—i.e., at-risk users . This article draws from more than 9 years of our work at Google to understand and support the digital safety of at-risk users—including survivors of intimate partner abuse, people involved with political campaigns, content creators, youth, and more—in technology intended for broad use. Among our learnings is that designing for inclusive digital safety across widely varied user needs and dynamic contexts is a wicked problem with no “correct” solution. Given this, we describe frameworks and design principles we have developed to help make at-risk research findings practically applicable to technologies intended for broad use and lessons we have learned about communicating them to practitioners.

The ever-increasing volume of archival data that needs to be reliably retained for long periods of time and the decreasing costs of disk storage, memory, and processing have motivated the design of low-cost, high-efficiency disk-based storage systems. However, managed disk storage is still expensive. To further lower the cost, redundancy can be eliminated with the use of interfile and intrafile data compression. However, it is not clear what the optimal strategy for compressing data is, given the diverse collections of data. To create a scalable archival storage system that efficiently stores diverse data, we present PRESIDIO, a framework that selects from different space-reduction efficent storage methods (ESMs) to detect similarity and reduce or eliminate redundancy when storing objects. In addition, the framework uses a virtualized content addressable store (VCAS) that hides from the user the complexity of knowing which space-efficient techniques are used, including chunk-based deduplication or delta compression. Storing and retrieving objects are polymorphic operations independent of their content-based address. A new technique, harmonic super-fingerprinting, is also used for obtaining successively more accurate (but also more costly) measures of similarity to identify the existing objects in a very large data set that are most similar to an incoming new object. The PRESIDIO design, when reported earlier, had comprehensively introduced for the first time the notion of deduplication, which is now being offered as a service in storage systems by major vendors. As an aid to the design of such systems, we evaluate and present various parameters that affect the efficiency of a storage system using empirical data.

We present the Deep Store archival storage architecture, a large-scale storage system that stores immutable data efficiently and reliably for long periods of time. Archived data is stored across a cluster of nodes and recorded to hard disk. The design differentiates itself from traditional file systems by eliminating redundancy within and across files, distributing content for scalability, associating rich metadata with content, and using variable levels of replication based on the importance or degree of dependency of each piece of stored data. We evaluate the foundations of our design, including PRESIDIO, a virtual content-addressable storage framework with multiple methods for interfile and intra-file compression that effectively addresses the data-dependent variability of data compression. We measure content and metadata storage efficiency, demonstrate the need for a variable-degree replication model, and provide preliminary results for storage performance.