Privacy-preserving solutions enable companies to offload confidential data to third-party services while fulfilling their government regulations. To accomplish this, they leverage various cryptographic techniques such as Homomorphic Encryption (HE), which allows performing computation on encrypted data. Most HE schemes work in a SIMD fashion, and the data packing method can dramatically affect the running time and memory costs. Finding a packing method that leads to an optimal performant implementation is a hard task. We present a simple and intuitive framework that abstracts the packing decision for the user. We explain its underlying data structures and optimizer, and propose a novel algorithm for performing 2D convolution operations. We used this framework to implement an inference operation over an encrypted HE-friendly AlexNet neural network with large inputs, which runs in around five minutes, several orders of magnitude faster than other state-of-the-art non-interactive HE solutions.

We use Apache Spark analytics to investigate the logs of an operational cloud object store service to understand how it is being used. This investigation involves going over very large amounts of historical data (PBs of records in some cases) collected over long periods of time retroactively. Existing tools, such as Elasticsearch-Logstash-Kibana (ELK), are mainly used for presenting short-term metrics and can-not perform advanced analytics such as machine learning. A possible solution is to save for long periods only certain aggregations or calculations produced from the raw log data, such as averages or histograms, however these must be decided in advance, and cannot be changed retroactively since the raw data has already been discarded. Spark allows us to gain insights going over historical data collected over long periods of time and to apply the historical models on online data in a simple and efficient way.

System simulation is a valuable tool to unveil inefficiencies and to test new strategies when implementing and revising systems. Often, simulations are parameterized using offline data and heuristic knowledge. Operational data, i.e., data gained through experimentation and observation, can greatly improve the fidelity between the actual system and the simulation. In a traffic scenario, for example, different road conditions or vehicle types can impact the outcome of the simulation and have to be considered during the modeling stage. This paper proposes using machine learning techniques to generate high fidelity simulation models. A traffic simulation case study exemplifies this approach by generating a model for the SUMO traffic simulator from vehicular telemetry data.

This paper defines a suite of requirements for future hybrid cosimulation standards, and specifically provides guidance for development of a hybrid cosimulation version of the Functional Mockup Interface (FMI). A cosimulation standard defines interfaces that enable diverse simulation tools to interoperate. Specifically, one tool defines a component that forms part of a simulation model in another tool. We focus on components with inputs and outputs that are functions of time, and specifically on mixtures of discrete events and continuous time signals. This hybrid mixture is not well supported by existing cosimulation standards, and specifically not by FMI 2.0, for reasons that are explained in this paper. The paper defines a suite of test components, giving a mathematical model of an ideal behavior, plus a discussion of practical implementation considerations. The discussion includes acceptance criteria by which we can determine whether a standard supports definition of each component. In addition, we define a set of test compositions that define requirements for coordination between components, including consistent handling of timed events.

The rising size and complexity of in-car networks call for more advanced and scalable verification solutions. We propose a verification methodology for in-car networks based on a system level test generator tool used for creating massive random biased stimuli, and on coverage and checking monitors. The test generator is an expert system based on an ontology of testing knowledge. A significant challenge is the continuous nature of the stimuli needed to represent the physical environment and the state of the internal components controlled by the vehicle's electronic systems. We report on applying our methodology to an example in-car network simulator.

In this paper, we explain how to achieve deterministic execution of FMUs (Functional Mockup Units) under the FMI (Functional Mockup Interface) standard. In particular, we focus on co-simulation, where an FMU either contains its own internal simulation algorithm or serves as a gateway to a simulation tool. We give conditions on the design of FMUs and master algorithms (which orchestrate the execution of FMUs) to achieve deterministic co-simulation. We show that with the current version of the standard, these conditions demand capabilities from FMUs that are optional in the standard and rarely provided by an FMU in practice. When FMUs lacking these required capabilities are used to compose a model, many basic modeling capabilities become unachievable, including simple discrete-event simulation and variable-step-size numerical integration algorithms. We propose a small extension to the standard and a policy for designing FMUs that enables deterministic execution for a much broader class of models. The extension enables a master algorithm to query an FMU for the time of events that are expected in the future. We show that a model can be executed deterministically if all FMUs in the model are either memoryless or implement one of rollback or step-size prediction. We show further that such a model can contain at most one “legacy” FMU that is not memoryless and provides neither rollback nor step-size prediction.