As post-quantum cryptography (PQC) nears standardization and eventual deployment, it is increasingly important to understand the security of the implementations of selected schemes. In this paper, we conduct such an investigation, uncovering concerning findings about many of the finalists of the NIST PQC standardization competition. Specifically, we show Rowhammer-based attacks on the Kyber and BIKE Key Exchange Mechanisms and the Dilithium Digital Signature scheme that enable complete recovery of the secret key with only a moderate amount of effort - no supercomputers, or months of precomputation. Moreover, we experimentally carry out our attacks using a combination of Rowhammer, performance degradation, and memory massaging techniques, showing that our attacks are practically feasible. Our results show that such side-channel based attacks are a critical concern and need to be considered when new cryptographic schemes are standardized, when standard implementations are developed, and when instances are deployed. We conclude with recommendations on implementation techniques that harden cryptographic schemes against Rowhammer attacks.

In this work, we recover the private key material of the FrodoKEM key exchange mechanism as submitted to the NIST Post Quantum Cryptography (PQC) standardization process.

Quantum computing-specifically Shor's algorithm [1]-presents an existential threat to some standard cryptographic algorithms. In preparation, post-quantum cryptography (PQC) algorithms have been in development and are nearing mathematical and cryptanalytic maturity. Standardization efforts through the National Institute of Standards and Technology (NIST) PQC standardization process have chosen one PKE/KEM algorithm (i.e., CRYSTALS-Kyber) and three digital signature algorithms (i.e., CRYSTALS-Dilithium, Falcon, and SPHINCS+). CRYSTALS-Kyber is a lattice-based, IND-CCA2-secure, key-encapsulation mechanism (KEM) based on the learning-with-errors problem over module lattices. This paper presents a masked hardware implementation of Kyber that is demonstrably secure against side-channel power analysis methods.

This work presents a prototype of an FPGA-based hand motion recognition system using a capacitive sensor array (CSA). The prototype system is being developed as a tool to evaluate upper-limb motor skills for assistive or rehabilitative applications. A light-weight gesture segmentation algorithm was developed that uses summation and moving average filtering of quantized capacitive sensing data to segment motions. The time-series hand motions are then recognized through a recurrent classifier based on long short-term memory (LSTM) neural networks. The classifier model is trained on uni-stroke hand written digit ('0'–'9') samples obtained from four volunteers. A total of 12,000 hand motion samples are collected. The accuracy of 10-fold and leave-one-user-out cross-validation accuracy is respectively 97.5% and 91.3% using a two-layer LSTM network. The LSTM classifier is implemented on a Zynq FPGA device. The experiment demonstrated that the FPGA implementation of the LSTM-based classifier can achieve real-time gesture classification with capacitive sensor data.

Recent efforts to integrate Internet Of Things (IoT) technologies into Smart City constructs have proven successful through proof-of-concept deployments in multiple cities. To facilitate additional deployments, standardization and dissemination of best practices for these technologies is critical. To that effort, this work seeks to establish a framework in a set of IoT hardware, software, and wireless radio components that are flexible to address several use cases and easily replicable to new deployments. Moreover, the framework aims to be standards-based and to use best practices for cybersecurity to maintain security and privacy. The framework is developed, deployed, and tested through three sub-projects in different domains and a live testbed housed by a municipal government, collecting over 160 million distinct sensor records from eight physical locations over a period of 8 months. Additionally, the framework is replicated to a separate jurisdiction to prove ease of replicability. This work identifies common barriers to IoT adoption and replication, both technological and organizational, and enumerates the approaches that were taken to overcome those barriers in these projects.

Safe Community Awareness and Alerting Network (SCALE) is a community government/academic/industry partnership effort that aims to deploy, actuate and evaluate techniques to support multiple heterogeneous IoT technologies in real world communities. SCALE2, an extension of SCALE, engages a multi-tier and multi-network approach to drive data flow from IoT devices to the cloud platforms. While devices are used to gather data, most of the analytics are executed in the cloud. Managing and utilizing these multiple networks, devices and technologies is a big challenge that calls for an integrated management. In this context, we propose to leverage a related effort, MINA (Multi-network INformation Architecture), that aims at integrating operations of multi-networks IoT deployments in a hierarchical manner. This paper discusses the mapping of the SCALE2 heterogeneous platforms in the MINA environment and argues for a hierarchical approach to extending and managing community IoT multi-networks. We discuss resilience methods that can be employed at different tiers in the hierarchical architecture. We illustrate examples of how multiple applications can be supported in this heterogeneous setting; example applications include cooperative seismic event detection, mobile data collections for air quality information and assisted living for elders. Finally, we discuss novel research challenges associated with managing multi-network IoT architecture.

Home automation and environmental control is a key ingredient of smart homes. While systems for home automation and control exist, there are few systems that interact with individuals suffering from paralysis, paresis, weakness and limited range of motion that are common sequels resulting from severe injuries such as stroke, brain injury, spinal cord injury and many chronic (guillian barre syndrome) and degenerative (amyotrophic lateral sclerosis) conditions. To address this problem, we present the design, implementation, and evaluation of Inviz, a low-cost gesture recognition system for paralysis patients that uses flexible textile-based capacitive sensor arrays for movement detection. The design of Inviz presents two novel research contributions. First, the system uses flexible textile-based capacitive arrays as proximity sensors that are minimally obtrusive and can be built into clothing for gesture and movement detection in patients with limited body motion. The proximity sensing obviates the need for touch-based gesture recognition that can cause skin abrasion in paralysis patients, and the array of capacitive sensors help provide better spatial resolution and noise cancellation. Second, Inviz uses a low-power hierarchical signal processing algorithm that breaks down computation into multiple low and high power tiers. The tiered approach provides maximal vigilance at minimal energy consumption. We have designed and implemented a fully functional prototype of Inviz and we evaluate it in the context of an end-to-end home automation system and show that it achieves high accuracy while maintaining low latency and low energy consumption.

Micro-harvesting from sources such as indoor light can enable a plethora of self-sustainable sensing systems for mobile healthcare applications. However, given the minuscule and variable amount of energy harvested from these renewable sources, practical sensing systems powered by micro-harvesting is today limited to light driven motion sensing. In this paper, we design, implement, and evaluate an indoor light driven wearable glove device that uses flex sensors and accelerometers for hand gesture recognition. Through the design, we make a two-fold contribution to micro-harvester driven mobile sensing systems. First, motivated by extensive profiling of panels for indoor light scavenging, we design a harvester that multiplexes panels of different compositions to maximally scavenge energy as a function of lighting conditions. Second, we present a tiered architecture composed of application specific hardware logic, wakeup controllers, a general purpose micro-controller, and a bluetooth device that can adapt to variable and ultra-low energy constraints, and at the same time provide high responsiveness and compute capability for gesture recognition. We evaluate the glove device in the context of a hand gesture driven home automation system for the elderly.