Data breaches represent a significant threat to organizations. While the general problem of protecting data has received much attention, one large (and growing) class has not - data that must be kept due to mandatory retention laws. Such data is often of little use to an organization, is rarely accessed, and represents a significant potential liability, yet cannot be discarded. Protecting such data entails an unusual combination of practical constraints (such as providing verification to a party that may be unknown) and thus requires functionality that is not well addressed by traditional cryptographic primitives. We propose to mitigate the risk to such data through a new system called Dragchute, which creates a time window during which locked data cannot be accessed by anyone. Based on a verifiable non-interactive, non-parallelizable, time-delay key escrow mechanism, Dragchute is novel in that it requires that no cryptographic material capable of providing early access to the data be retained, yet provides verification for multiple properties. We define a base construction for Dragchute, show possible extensions that help meet additional verification requirements, and characterize its performance. Our results show that Dragchute systems offer verifiable, customizable, computational protection against data exposure for encryption costs similar to traditional methods (e.g., less than 6% overhead compared to AEAD). We thus show that Dragchute systems provide a critical new means for protecting data that must be retained long term due to mandatory retention laws.

This paper describes our implementation and experience of incorporating computer science concepts into a team-taught, first-year interdisciplinary course for prospective science majors at the University of Richmond. The course integrates essential concepts from each of five STEM disciplines: biology, chemistry, computer science, mathematics, and physics. Including computer science in this course faces three primary challenges: few of the students have any CS background; the time devoted to CS instruction is reduced compared to a traditional introductory CS course; and the spirit of the course requires the CS material to be highly integrated with the other disciplines. Here we discuss our experience from three-plus years of offering the course and its impact on the major/minor pool of students in our own discipline.

Volunteer distributed computations utilize spare processor cycles of personal computers that are connected to the Internet. The related computation integrity concerns are commonly addressed by assigning tasks redundantly. Aside from the additional computational costs, a significant disadvantage of redundancy is its vulnerability to colluding adversaries. This paper presents a tunable redundancy-based task distribution strategy that increases resistance to collusion while significantly decreasing the associated computational costs. Specifically, our strategy guarantees a desired cheating detection probability regardless of the number of copies of a specific task controlled by the adversary. Though not the first distribution scheme with these properties, the proposed method improves upon existing strategies in that it requires fewer computational resources. More importantly, the strategy provides a practical lower bound for the number of redundantly assigned tasks required to achieve a given detection probability

The past few years have seen the development of distributed computing platforms designed to utilize the spare processor cycles of a large number of personal computers attached to the Internet in an effort to generate levels of computing power normally achieved only with expensive supercomputers. Such large scale distributed computations running in untrusted environments raise a number of security concerns, including the potential for intentional or unintentional corruption of computations, and for participants to claim credit for computing that has not been completed. This paper presents two strategies for hardening selected applications that utilize such distributed computations. Specifically, we show that carefully seeding certain tasks with precomputed data can significantly increase resistance to cheating (claiming credit for work not computed) and incorrect results. Similar results are obtained for sequential tasks through a strategy of sharing the computation of N tasks among K>N nodes. In each case, the associated cost is significantly less than the cost of assigning tasks redundantly.