Eye tracking is routinely being incorporated into virtual reality (VR) systems. Prior research has shown that eye tracking data, if exposed, can be used for re-identification attacks [14]. The state of our knowledge about currently existing privacy mechanisms is limited to privacy-utility trade-off curves based on data-centric metrics of utility, such as prediction error, and black-box threat models. We propose that for interactive VR applications, it is essential to consider user-centric notions of utility and a variety of threat models. We develop a methodology to evaluate real-time privacy mechanisms for interactive VR applications that incorporate subjective user experience and task performance metrics. We evaluate selected privacy mechanisms using this methodology and find that re-identification accuracy can be decreased to as low as 14% while maintaining a high usability score and reasonable task performance. Finally, we elucidate three threat scenarios (black-box, black-box with exemplars, and white-box) and assess how well the different privacy mechanisms hold up to these adversarial scenarios. This work advances the state of the art in VR privacy by providing a methodology for end-to-end assessment of the risk of re-identification attacks and potential mitigating solutions. f.

Modern Data-Intensive Scalable Computing (DISC) systems are designed to process data through batch jobs that execute programs (e.g., queries) compiled from a high-level language. These programs are often developed interactively by posing ad-hoc queries over the base data until a desired result is generated. We observe that there can be significant overlap in the structure of these queries used to derive the final program. Yet, each successive execution of a slightly modified query is performed anew, which can significantly increase the development cycle. Vega is an Apache Spark framework that we have implemented for optimizing a series of similar Spark programs, likely originating from a development or exploratory data analysis session. Spark developers (e.g., data scientists) can leverage Vega to significantly reduce the amount of time it takes to re-execute a modified Spark program, reducing the overall time to market for their Big Data applications.

Developers use cloud computing platforms to process a large quantity of data in parallel when developing big data analytics. Debugging the massive parallel computations that run in today's data-centers is time consuming and error-prone. To address this challenge, we design a set of interactive, real-time debugging primitives for big data processing in Apache Spark, the next generation data-intensive scalable cloud computing platform. This requires re-thinking the notion of step-through debugging in a traditional debugger such as gdb, because pausing the entire computation across distributed worker nodes causes significant delay and naively inspecting millions of records using a watchpoint is too time consuming for an end user. First, BIGDEBUG's simulated breakpoints and on-demand watchpoints allow users to selectively examine distributed, intermediate data on the cloud with little overhead. Second, a user can also pinpoint a crash-inducing record and selectively resume relevant sub-computations after a quick fix. Third, a user can determine the root causes of errors (or delays) at the level of individual records through a fine-grained data provenance capability. Our evaluation shows that BIGDEBUG scales to terabytes and its record-level tracing incurs less than 25% overhead on average. It determines crash culprits orders of magnitude more accurately and provides up to 100% time saving compared to the baseline replay debugger. The results show that BIGDEBUG supports debugging at interactive speeds with minimal performance impact.

Debugging data processing logic in Data-Intensive Scalable Computing (DISC) systems is a difficult and time consuming effort. Today's DISC systems offer very little tooling for debugging programs, and as a result programmers spend countless hours collecting evidence ( e.g. , from log files) and performing trial and error debugging. To aid this effort, we built Titian , a library that enables data provenance ---tracking data through transformations---in Apache Spark. Data scientists using the Titian Spark extension will be able to quickly identify the input data at the root cause of a potential bug or outlier result. Titian is built directly into the Spark platform and offers data provenance support at interactive speeds---orders-of-magnitude faster than alternative solutions---while minimally impacting Spark job performance; observed overheads for capturing data lineage rarely exceed 30% above the baseline job execution time.

In software-defined networking (SDN), a software controller manages a distributed collection of switches by installing and uninstalling packet-forwarding rules in the switches. SDNs allow flexible implementations for expressive and sophisticated network management policies. We consider the problem of verifying that an SDN satisfies a given safety property. We describe Kuai, a distributed enumerative model checker for SDNs. Kuai takes as input a controller implementation written in Murphi, a description of the network topology (switches and connections), and a safety property, and performs a distributed enumerative reachability analysis on a cluster of machines. Kuai uses a set of partial order reduction techniques specific to the SDN domain that help reduce the state space dramatically. In addition, Kuai performs an automatic abstraction to handle unboundedly many packets traversing the network at a given time and unboundedly many control messages between the controller and the switches. We demonstrate the scalability and coverage of Kuai on standard SDN benchmarks. We show that our set of partial order reduction techniques significantly reduces the state spaces of these benchmarks by many orders of magnitude. In addition, Kuai exploits large-scale distribution to quickly search the reduced state space.

In a common use case for cloud computing, clients upload data and computation to servers that are managed by a third-party infrastructure provider. We describe MrCrypt, a system that provides data confidentiality in this setting by executing client computations on encrypted data. MrCrypt statically analyzes a program to identify the set of operations on each input data column, in order to select an appropriate homomorphic encryption scheme for that column, and then transforms the program to operate over encrypted data. The encrypted data and transformed program are uploaded to the server and executed as usual, and the result of the computation is decrypted on the client side. We have implemented MrCrypt for Java and illustrate its practicality on three standard benchmark suites for the Hadoop MapReduce framework. We have also formalized the approach and proven several soundness and security guarantees.