As frequent users of social networking applications, middle and high school students are well-suited for curricular interventions that leverage these technologies. Allowing students to see "behind the curtain" of these applications provides them with a unique opportunity to better understand the discipline of computer science upon which these technologies are built and influences their perceptions of computer security and privacy. We present a novel social networking simulation that allows students to create a social network account, including profile data and images, and to manage privacy settings and friend connections. The platform, named Fakesbook, presents students with a visualization of the social network as a graph, enabling them to observe the spread of profile data (theirs and others') depending on friend connections and choices of privacy settings. We additionally present our lab curriculum which uses Fakesbook to enable active learning and adversarial thinking to engage students and build agency with regard to privacy and computing concepts. We deployed and, over several years, evaluated our platform and curriculum with hundreds of students from a diverse set of backgrounds at educational events designed to introduce these populations to computer science, cybersecurity, and privacy. Survey results indicate that students gained or deepened their understanding of online privacy and security and that 86% of participants found that Fakesbook helped them "think about privacy and computer security."

Since 2010, the Cal Poly Computer Science Department has required computing majors to select from a variety of CS0 courses to start their academic year. The broad objective of the course is to attract and retain undergraduates that have no prior experience in CS by using authentic problems that demonstrate the relevance and highlight the role of computers in solving "real world" problems. The course is offered in a variety of thematic "flavors" that leverage a student's pre-existing interests (e.g. in music or art), but all share the common goals of introducing students to the basics of programming, teamwork, and college-level study. While there is overlap in overall goals, the courses vary drastically in topic matter (e.g. robotics, gaming, music, computational art, mobile apps, security) and in pedagogical approach (e.g. principles of design, project-based student driven learning, and traditional topic-based programming modules). The introduction of this CS0 course has increased students' commitment to their major and success in follow-on classes. We present these successes and show that student GPAs in a follow-on object oriented programming course do not vary significantly for the differing subtopics and teaching pedagogies employed in the various flavors. Our report includes examining two student subgroups (those experienced with programming and those new to programming). Our evaluations suggest that the existence and goal of the course matter more than the specific content, with all subtopics and pedagogical approaches performing well.

We introduce and analyze a general framework for authentically binding data to a location while providing strong assurances against cloud storage providers that (either accidentally or maliciously) attempt to re-locate cloud data. We then evaluate a preliminary solution in this framework that combines constraint-based host geolocation with proofs of data possession, called constraint-based data geolocation (CBDG). We evaluate CBDG using a combination of experiments with PlanetLab and real cloud storage services, demonstrating that we can bind fetched data to the location originally hosting it with high precision. We geolocate data hosted on the majority of our PlanetLab targets to regions no larger than 118,000 km^2, and we geolocate data hosted on Amazon S3 to an area no larger than 12,000 km^2, sufficiently small to identify the state or service region.

We introduce a model for provable data possession (PDP) that can be used for remote data checking: A client that has stored data at an untrusted server can verify that the server possesses the original data without retrieving it. The model generates probabilistic proofs of possession by sampling random sets of blocks from the server, which drastically reduces I/O costs. The client maintains a constant amount of metadata to verify the proof. The challenge/response protocol transmits a small, constant amount of data, which minimizes network communication. Thus, the PDP model for remote data checking is lightweight and supports large data sets in distributed storage systems. The model is also robust in that it incorporates mechanisms for mitigating arbitrary amounts of data corruption. We present two provably-secure PDP schemes that are more efficient than previous solutions. In particular, the overhead at the server is low (or even constant), as opposed to linear in the size of the data. We then propose a generic transformation that adds robustness to any remote data checking scheme based on spot checking. Experiments using our implementation verify the practicality of PDP and reveal that the performance of PDP is bounded by disk I/O and not by cryptographic computation. Finally, we conduct an in-depth experimental evaluation to study the tradeoffs in performance, security, and space overheads when adding robustness to a remote data checking scheme.

We introduce a model for provable data possession (PDP) that allows a client that has stored data at an untrusted server to verify that the server possesses the original data without retrieving it. The model generates probabilistic proofs of possession by sampling random sets of blocks from the server, which drastically reduces I/O costs. The client maintains a constant amount of metadata to verify the proof. The challenge/response protocol transmits a small, constant amount of data, which minimizes network communication. Thus, the PDP model for remote data checking supports large data sets in widely-distributed storage system.

The ext3cow file system, built on the popular ext3 file system, provides an open-source file versioning and snapshot platform for compliance with the versioning and audtitability requirements of recent electronic record retention legislation. Ext3cow provides a time-shifting interface that permits a real-time and continuous view of data in the past. Time-shifting does not pollute the file system namespace nor require snapshots to be mounted as a separate file system. Further, ext3cow is implemented entirely in the file system space and, therefore, does not modify kernel interfaces or change the operation of other file systems. Ext3cow takes advantage of the fine-grained control of on-disk and in-memory data available only to a file system, resulting in minimal degradation of performance and functionality. Experimental results confirm this hypothesis; ext3cow performs comparably to ext3 on many benchmarks and on trace-driven experiments.