Anthony D. Joseph - US grants

The PIs propose to increase the effectiveness of the U.C. Berkeley Computer Science Department teaching faculty and to extend the reach of the program well beyond the confines of the campus and the duration of a traditional academic career. In particular, the proposed approach will take advantage of problem-based learning to maintain a high standard of quality and will utilize electronic tools that can support the special time and space requirements of off-campus learners.

By incorporating the principles and knowledge of problem-based learning, learner-centered design, computer-supported cooperative work, and current research projects in the department, an informed design of a suite of learning, collaboration, and awareness tools will be developed, including:

Enhanced lecture viewing tools, including secondary discussion channel chat and participation, TVI and DTVI, asynchronous discussion groups, and analysis tools
Media sharing tools with asynchronous discussion and scaffolding tools to support group work, including integration of note-taking tools that are searchable across a number of classes and projects
Group awareness tools for keeping students informed about what others in their group are working on, how much they have accomplished, as well as what other groups are doing
Monitoring tools to track electronic communications & tool usage by student groups
Instructor awareness tools for identifying group progress and lack of progress in achieving project and learning goals, including analysis tools using data generated from the monitoring tools

This proposal creates an experimental infrastructure network to support the development and demonstration of next-generation information security technologies for cyber defense. This cyber Defense Technology Experimental Research Network (DETER Network) will provide the necessary infrastructure -- networks, tools, methodologies and supporting process -- to support national-scale experimentation on emerging security research and advanced development technologies. The DETER network will be designed and operated to ensure direct participation from government entities and their sponsored researchers in a wide and varied community. Early activities and deliverables will describe policies procedures for use of the experimental facility along with a users guide. The work will facilitate scientific experimentation and validation against established baselines of attack behavior and allow experimental approaches that involve breaking the network infrastructure. The DETER network will promote and catalyze expanded research and commercialization efforts in this vital area.

Intellectual Merit: The proposal will develop architectures for test bed networks that are representative of the Internet itself at a somewhat smaller scale. It will leverage work from ACIR at the International Computer Science Institute, Berkeley on traffic generation for use in the DETER network. The development of both of these is itself a significant research challenge. On versions of the DETER network the researcher will study cyber security solutions to Distributed Denial of Service, Worm Defense and other network attacks through a combination of experiments, emulation, and analytical solutions. The aim is to integrate analytic methods with experiments on networks of adequate scale and complexity so as to be able to have confidence in solutions and transfer them to industrial partners participating in the test bed through their equipment.

Broader impact: This proposal is expected to be the key building block for bringing network security initially against Distributed Denial of Service and then Worm attacks but in general all intrusions. The testbed network will be integrated into course work at both the upper division and graduate level Berkeley, USC and at partner institutions at UC Davis, Purdue, and Penn State.

This project investigates new ways of structuring and securing information by using cryptographically hardened bundles of data, called DataCapsules. The need for a new approach stems from the proliferation of data-driven technology and cyber-physical systems that control physical devices, such as robots and manufacturing machines, and use information from widely disparate sources. The consequences of data exposure, breach, or corruption can lead to identity theft, property loss, or (increasingly) bodily harm. Unfortunately, common approaches to protecting information are ad-hoc, buggy, and subject to a variety of attacks and failure modes. In contrast, the DataCapsule infrastructure provides a standardized approach to sequencing, securing every bit of information while also including explicit provenance (stating who generated it). DataCapsules may move freely from place to place in the network while retaining their integrity, thereby enabling secure computation at the edge of the network. Further, this project investigates techniques to ease the transition of application writers from current practice to use of the DataCapsule infrastructure. The benefits of standardization around DataCapsules are many fold, including (1) more uniform application of best practices for data security; (2) secure edge computing infrastructures that fluidly interact with authorized entities in the core of the network (cloud); and (3) an opportunity for new networking environments that respect information privacy and security while optimizing for performance and quality of service.

This project explores the use of DataCapsules to improve the security and performance of robotic and machine-learning applications operating in edge computing environments. DataCapsules are secured bundles of information with unique, self-certifying names that are transported over a data-centric 'narrow-waist' infrastructure called the Global Data Plane (GDP). This project investigates the design of DataCapsules as well as an architecture for the GDP that provides flat-address routing from authorized clients to DataCapsules, allowing DataCapsules to be replicated and reside anywhere within the GDP. DataCapsules consist of standardized metadata wrappers anchoring hash-chain-linked histories of transactions labeled by signatures. As universal 'ground-truths' for data storage applications, DataCapsules share some advantages of block-chains, including publicly verifiable integrity. Above the DataCapsule layer, application writers benefit from uniform security while continuing to utilize common storage access patterns, such as filesystems, databases, and key-value stores. The GDP partitions the network into Trust Domains (TDs) to allow clients to reason about the trustworthiness of hardware. This architecture includes overlay switches connected via a tunneling protocol and a scalable location resolution infrastructure. Each TD is responsible for a subset of the DataCapsules and provides data location facilities that serve 'location delegation' certificates (mapping names to network locations) for these DataCapsules. For scalability, this project investigates several name resolution mechanisms, including one based on distributed hash table (DHT) principles. This project also utilizes secure enclave technologies (e.g. Intel SGX) to provide secure computation at the edge of the network. By promoting best practice labeling and secure management of information, the DataCapsule infrastructure promises to lead to an overall reduction in data breaches and safer public and private cyberspace infrastructure. Further, it will allow application writers to trust the security of information at the edge of the network, thus leading to new and better application of data-driven techniques at the network edge while simultaneously improving privacy; this, in turn, will lead to better applications, such as robotic and smart manufacturing. Finally, in addition to educational activities, the project, in collaboration with the University of California Berkeley's Lawrence Hall of Science, will produce open-access videos to raise awareness of information vulnerability and provenance with youth and the public at large.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.