Abstract Every deployed DoD system undergoes certification (or qualification, for military) to assess the software system’s fitness for use. Certification requires that human subject matter expert look over evidence and evaluate its conformance to standards such as DO-178C or the Risk Management Framework (RMF). Current practices are not keeping pace with the ever-increasing size of software systems and the amount of evidence required for their certification. This problem is further exasperated when platforms are comprised of systems of systems developed by multiple suppliers, each providing data generated by different tools, in different formats, and captured with different granularity. We demonstrate the application of W3C Semantic web technologies to perform efficient evidence curation under a military research program. This tech stack offers the right solutions for integrating data from heterogeneous sources and for performing graph-traversal queries across data that changes at a regular frequency.

Sexual misconduct is prevalent in workplace and education settings but stigma and risk of further damage deter many victims from seeking justice. Callisto, a non-profit that has created an online sexual assault reporting platform for college campuses, is expanding its work to combat sexual assault and harassment in other industries. In this new product, users will be invited to an online "matching escrow" that will detect repeat perpetrators and create pathways to support for victims. Users submit encrypted data about their perpetrator, and this data can only be decrypted by the Callisto Options Counselor (a lawyer), when another user enters the identity of the same perpetrator. If the perpetrator identities match, both users will be put in touch independently with the Options Counselor, who will connect them to each other (if appropriate) and help them determine their best path towards justice. The client relationships with the Options Counselors are structured so that any client-counselor communications would be privileged. A combination of client-side encryption, encrypted communication channels, oblivious pseudo-random functions, key federation, and Shamir Secret Sharing keep data confidential in transit, at rest, and during the matching process with the guarantee that only the lawyer ever has access to user submitted data, and even then only when a match is identified.

Anomaly detectors are often used to produce a ranked list of statistical anomalies, which are examined by human analysts in order to extract the actual anomalies of interest. This can be exceedingly difficult and time consuming when most high-ranking anomalies are false positives and not interesting from an application perspective. In this paper, we study how to reduce the analyst's effort by incorporating their feedback about whether the anomalies they investigate are of interest or not. In particular, the feedback will be used to adjust the anomaly ranking after every analyst interaction, ideally moving anomalies of interest closer to the top. Our main contribution is to formulate this problem within the framework of online convex optimization, which yields an efficient and extremely simple approach to incorporating feedback compared to the prior state-of-the-art. We instantiate this approach for the powerful class of tree-based anomaly detectors and conduct experiments on a range of benchmark datasets. The results demonstrate the utility of incorporating feedback and advantages of our approach over the state-of-the-art. In addition, we present results on a significant cybersecurity application where the goal is to detect red-team attacks in real system audit data. We show that our approach for incorporating feedback is able to significantly reduce the time required to identify malicious system entities across multiple attacks on multiple operating systems.

We discuss the widely increasing range of applications of a cryptographic technique called multi-party computation. For many decades, this was perceived to be of purely theoretical interest, but now it has started to find application in a number of use cases. We highlight in this paper a number of these, ranging from securing small high-value items such as cryptographic keys, through to securing an entire database.

Secure multilinear maps (mmaps) have been shown to have remarkable applications in cryptography, such as multi-input functional encryption (MIFE) and program obfuscation. To date, there has been little evaluation of the performance of these applications. In this paper we initiate a systematic study of mmap-based constructions. We build a general framework, called 5Gen, to experiment with these applications. At the top layer we develop a compiler that takes in a high-level program and produces an optimized matrix branching program needed for the applications we consider. Next, we optimize and experiment with several MIFE and obfuscation constructions and evaluate their performance. The 5Gen framework is modular and can easily accommodate new mmap constructions as well as new MIFE and obfuscation constructions, as well as being an open-source tool that can be used by other research groups to experiment with a variety of mmap-based constructions.