The limited availability of high-quality computer networking data, and the privacy risks of sharing what does exist, has prompted development of ML-based methods for generating synthetic network data that mimics real communication between networked devices. The viability of these models hinges on both the quality of their output and how well they preserve private information encoded in their training data. Prior work has sought to address this by training models with differential privacy (DP). However, how this choice affects the actual privacy of the training data, and subsequently the quality of the generated output, is not well understood. In this work, we analyze the relationship between privacy and quality in generative network data models. Using the success of membership inference attacks (MIAs) as the metric for privacy, we observe that whether DP mitigates MIAs depends heavily on model architecture and representation of network data used for training. In particular, we empirically find that some approaches to generating synthetic network data train models that heavily skew towards either overgeneralizing or undergeneralizing to their training data, resulting in poor or inconsistent MIA performance. In these cases, using DP does not yield substantive improvements in vulnerability to MIAs. As for the quality of generated data, we find that DP synthetic network data can retain statistical similarity to real data even under strict privacy budgets, and that downstream models (e.g., classifiers, regressors) trained on this data tend to achieve at least as good accuracy as models trained on non-DP data. These results suggest that DP, depending on the model, offers protection against MIAs without degrading the utility of the generated output, and in some cases, improves utility.

Smart homes contain diverse sensors and actuators controlled by IoT apps that provide custom automation. Prior works showed that an adversary could exploit physical interaction vulnerabilities among apps and put the users and environment at risk, e.g., to break into a house, an adversary turns on the heater to trigger an app that opens windows when the temperature exceeds a threshold. Currently, the safe behavior of physical interactions relies on either app code analysis or dynamic analysis of device states with manually derived policies by developers. However, existing works fail to achieve sufficient breadth and fidelity to translate the app code into their physical behavior or provide incomplete security policies, causing poor accuracy and false alarms.

Concern for the environment and energy security is changing the way we think about energy. Grid-enabled passenger vehicles, like electric vehicles (EV) and plug-in hybrid electric vehicles (PHEV) can help address environmental and energy issues. Automakers have recognized that electric drive vehicles are critical to the future of the industry. However, some challenges exist to greater adoption: the perception of cost, EV range, access to charging, potential impacts to the grid, and lack of public awareness about the availability and practicality of these vehicles. Although the current initial price for EV's is higher, their operating costs are lower. Policies that reduce the total cost of ownership of EVs and PHEVs, compared to conventional internal combustion engine (ICE) vehicles, will lead to faster market penetration. Greater access to charging infrastructure will also accelerate public adoption. Smart grid technology will optimize the vehicle integration with the grid, allowing intelligent and efficient use of energy. By coordinating efforts and using a systems perspective, the advantages of EVs and PHEVs can be achieved using the least resources. This paper analyzes these factors, their rate of acceleration and how they may synergistically align for the electrification of vehicles.

Many places in the world are too rugged or enclosed for vehicles to access. Even today, material transport to such areas is limited to manual labor and beasts of burden. Modern advancements in wearable robotics may make those methods obsolete. Lower extremity exoskeletons seek to supplement the intelligence and sensory systems of a human with the significant strength and endurance of a pair of wearable robotic legs that support a payload. This paper outlines the use of Clinical Gait Analysis data as the framework for the design of such a system at UC Berkeley.

The first energetically autonomous lower extremity exoskeleton capable of carrying a payload has been demonstrated at U.C. Berkeley. This paper summarizes the mechanical design of the Berkeley Lower Extremity Exoskeleton (BLEEX). The anthropomorphically-based BLEEX has seven degrees of freedom per leg, four of which are powered by linear hydraulic actuators. The selection of the degrees of freedom and their ranges of motion are described. Additionally, the significant design aspects of the major BLEEX components are covered.