Researchers at the University of California Davis have developed a technology that enables the provable editing of DNNs (deep neural networks) to meet specified safety criteria without altering their architecture.

Researchers at the University of California, Davis have developed a technology that introduces a novel approach to hardware security using photonic physically unclonable functions for true random number generation and biometric ID.

Brief description not available

Metadata is used to summarize basic information about data that can make tracking and working with specific data easier. Today’s communication systems, like WhatsApp, iMessage, and Signal, use end-to-end encryption to protect message contents. Such communication systems do not hide metadata, which is the data providing information about one or more aspects of such contents, like messages. Such metadata includes information about who communicates with whom, when, and how much, and is generally visible to systems and network observers. As a result, cyber risk associated with metadata leakage and traffic analysis remains a significant attack vector in such modern communication systems. Previous attempts to address this risk have been generally seen as not secure or prohibitively expensive, for example, by imposing inflexible bandwidth restrictions and cumbersome synchronous schedules globally, which cripples performance. Moreover, prior approaches relied on distributed trust for security, which is largely incompatible with conventional organizations hosting or using such apps.

Brief description not available

Networks of connectivity-enabled devices, known as internet of things or IoT, involve interrelated devices that connect and exchange data with other IoT devices and the cloud. As the number of IoT devices and their applications continue to significantly increase, managing and administering edge and access networks have become increasingly more challenging. Currently, there are approximately 31 billion ‘‘things’’ connected to the internet, with a projected rise to 75 billion devices by 2025. Because of IoT interconnectivity and ubiquitous device use, assessing the risks, designing/specifying what’s reasonable, and implementing controls can be overwhelming to conventional frameworks. Any approach to better IoT network security, for example by improved detection and denial or restriction of access by unauthorized devices, must consider its impact on performance such as speed, power use, interoperability, and scalability. The IoT network’s physical and MAC layers are not impenetrable and have many known threats, especially identity-based attacks such as MAC spoofing events. Common network infrastructure uses WPA2 or IEEE 802.11i to help protect users and their devices and connected infrastructure. However, the risk of MAC spoofing remains, as bad actors leverage public tools on 802.11 commodity hardware, or intercept sensitive data packets at scale, to access users physical layer data, and can lead to wider tampering and manipulation of hardware-level parameters.

Brief description not available

 Efficiency gains for a few sample applications; CGM = Continuous Glucose Monitor; MSS = Mergeable Stateful Signatures.

Brief description not available

Brief description not available

Researchers at the University of California, Davis have developed an approach for integrating quantum computers into the existing internet backbone.

Researchers at UCI have developed a novel co-design methodology of hardware-software architecture used for protecting Hall sensors found in autonomous vehicles, smart grids, industrial plants, etc…, against spoofing attacks.There are currently no comprehensive measures in place to protecting Hall sensors.

Researchers at the University of California, Davis have developed a multi-wavelength, Spiking, Nanophotonic, Neural Reservoir Computing (SNNRC) system with high-dimensional (HD) computing capability.

Researchers from the Department of Communication at UCLA have developed a system for identifying and eliminating clickbait from social media.

UCLA researchers in the Department of Computer Science have developed a new privacy preserving mechanism for stream analytics.

Researchers at UCI have developed a novel method for encrypting optical communications, which is simpler, less expensive, and less computationally-demanding than standard solutions.

The invention is a technique that provides a novel, reliable and secure cryptography solution for inter-vehicular visible light communication. Through combining unique data as the road roughness and the driving behavior, a symmetric security key is generated for both communicating vehicles. As the data used is unique to the communicating vehicles only, the generated keys are thus unique, securing a reliable communication channel between both vehicles.

As society becomes more and more complicated, we have also developed ways to analyze and solve some of these complexities via the convergence of the fields of artificial intelligence, cognitive science and neuroscience. What has emerged is the development of machine learning, which allows computers to improve automatically through experience. Thus, developers working on artificial intelligence (AI) systems have come forth to align AI with machine-learning algorithms to cover a wide variety of machine-learning problems. The most advanced of these are called supervised learning methods which form their predictions via learned mapping, which can include decision trees, logistic regression, support vector machines, neural networks and Bayesian classifiers. More recently, deep networks have emerged as multilayer networks involved in a number of applications, such as computer vision and speech recognition. A practical concern in the rush to adopt AI as a service is the capability to perform model protection: AI models are usually trained by allocating significant computational resources to process massive amounts of training data. The built models are therefore considered as the owner’s intellectual property (IP) and need to be protected to preserve the competitive advantage.

Just-in-time compilation is a method of executing computer code which, while boasting superior execution times, is prone to security exploits. UCI researchers have developed librando, a software framework for increasing security for just-in-time compilers, ensuring that generated program code is not predictable to an attacker.

A collaboration between UCLA and Rutgers have developed a novel information flow tracking technique to detect potential data leaks in mobile devices.

Brief description not available

Brief Description: UCR researchers have developed an innovative multifunctional on-chip sensor for comprehensive detection of counterfeited ICs. Their original on-chip invention could measure usage age via electromigration, but they have improved upon the accuracy of this readout by implementing antifuse memory block and combining two aging sensors: RO-based and EM-based. To enhance security even further, they applied corresponding post-fabrication methods of registering ICs with unique IDs so that activation can only occur once matched up to the ID embedded in the antifuse memory component.

The technology is a software architecture for providing robust predictions for software systems from cloud sourced data points. Properties include:the ability to “wrap” existing software sensors with additional services. The technology is used by executing software on a cloud based server and manipulating data points from user update systems, such as Waze, and provide predictive services around these data points.

The technology is a coding process which facilitates efficient data failure recovery in cloud storage systems.It features greater flexibility in choosing subset of storage nodes for recovery and reduces amount of data that must be transferred upon recovery.

Researchers at the University of California, Davis have developed a method using standard mobile device sensors assisting with facial authentication to overcome the limitations faced by current methods.