This work explores how to physically watermark images generated by CMOS cameras using deliberately injected radio-frequency signals. CMOS camera imaging is ubiquitous in embedded systems such as smartphones, AR/VR headsets, drones, and other IoT platforms to capture photos and videos. In restricted environments, a property owner may wish to prevent unauthorized camera recordings depending on spatio-temporal context. Indelible watermarks can deter unauthorized recording. A key research challenge is how to find a reasonably general mechanism to surreptitiously inject watermarks without access to the camera. Existing methods typically rely on software-based watermarking or metadata generation, assuming cooperation from camera owners. However, adversaries can trivially disable metadata or watermarking functions to evade forensic analysis. To address this gap, our work explores an unconventional approach of watermarking non-cooperative cameras by injecting radio-frequency interference in the environment to affect the analog sensing process and inject defender-controlled patterns in the image output. Our analysis explains how the rolling shutter and Bayer filter hardware convert radio-frequency signals into color stripes with variable widths. Building upon model-based simulation, our prototype design encodes and extracts imperceptible watermarks with a bandwidth of up to 50 bits per image. Proof-of-concept evaluations in lab environments show that the proposed technique could support watermarking images with diverse background scenes and reveal future challenges of improving watermark bandwidth and injection distance.

Peer Reviewed

This paper studies vulnerabilities at the intersection of wearable devices and automated control systems. Particularly, we focus on exploiting smart glasses as an entry point and unveil the threats of taking over security-critical automated control chains without user verification or interaction. These vulnerabilities can be especially pertinent in scenarios where security mechanisms only depend on entry point security with minimal user verification (relying on complete trust over previous nodes in automated control chains). We have validated the effects of our attacks on real-world systems (e.g., Tesla vehicles) that are controlled by software and automation tools such as Apple Shortcuts or IFTTT. We show how our contactless, speaker-independent, and electromagnetic interference based attacks can control functionalities such as unlocking doors and initiating remote start of Tesla vehicles, even though the victim’s phone is in a lock-screen status. Our findings not only demonstrate the potential for unauthorized control over automated, connected systems but also highlight the urgent need for more robust security measures in the integration of wearable technology with broader automation frameworks.

Autonomous vehicles increasingly rely on camera-based computer vision systems to perceive environments and make critical driving decisions. To improve image quality, image stabilizers with inertial sensors are added to reduce image blurring caused by camera jitters. However, this trend creates a new attack surface. This paper identifies a system-level vulnerability resulting from the combination of emerging image stabilizer hardware susceptible to acoustic manipulation and computer vision algorithms subject to adversarial examples. By emitting deliberately designed acoustic signals, an adversary can control the output of an inertial sensor, which triggers unnecessary motion compensation and results in a blurred image, even when the camera is stable. These blurred images can induce object misclassification, affecting safety-critical decision-making. We model the feasibility of such acoustic manipulation and design an attack framework that can accomplish three types of attacks: hiding, creating, and altering objects. Evaluation results demonstrate the effectiveness of our attacks against five object detectors (YOLO V3/V4/V5, Faster R-CNN, and Apollo) and two lane detectors (UFLD and LaneAF). We further introduce the concept ofAMpLeattacks, a new class of system-level security vulnerabilities resulting from a combination of adversarial machine learning and physics-based injection of information-carrying signals into hardware.

Smartphone motion sensors provide cybersecurity attackers with a stealthy way to eavesdrop on nearby acoustic information. Eavesdropping on touchtones emitted by smartphone speakers when users input numbers into their phones exposes sensitive information such as credit card information, banking PINs, and social security card numbers to malicious applications with access to only motion sensor data. This work characterizes this new security threat of touchtone eavesdropping by providing an analysis based on physics and signal processing theory. We show that advanced adversaries who selectively integrate data from multiple motion sensors and multiple sensor axes can achieve over 99% accuracy on recognizing 12 unique touchtones. We further design, analyze, and evaluate several mitigations which could be implemented in a smartphone update. We found that some apparent mitigations such as low-pass filters can undesirably reduce the motion sensor data to benign applications by 83% but only reduce an advanced adversary’s accuracy by less than one percent. Other more informed designs such as anti-aliasing filters can fully preserve the motion sensor data to support benign application functionality while reducing attack accuracy by 50.1%.

In today’s era of the Internet of Things, we are surrounded by security- and safety-critical, network-connected devices. In parallel with the rise in attacks on such devices, we have also seen an increase in devices that are abandoned, reached the end of their support periods, or will not otherwise receive future security updates. While this issue exists for a wide array of devices, those that use monolithic firmware, where the code and data are opaquely intermixed, have traditionally been difficult to examine and protect.

Our research discovers how the rolling shutter and movable lens structures widely found in smartphone cameras modulate structure-borne sounds onto camera images, creating a point-of-view (POV) optical-acoustic side channel for acoustic eavesdropping. The movement of smartphone camera hardware leaks acoustic information because images unwittingly modulate ambient sound as imperceptible distortions. Our experiments find that the side channel is further amplified by intrinsic behaviors of Complementary Metal-oxide–Semiconductor (CMOS) rolling shutters and movable lenses such as in Optical Image Stabilization (OIS) and Auto Focus (AF). Our paper characterizes the limits of acoustic information leakage caused by structure-borne sound that perturbs the POV of smartphone cameras. In contrast with traditional optical-acoustic eavesdropping on vibrating objects, this side channel requires no line of sight and no object within the camera’s field of view (images of a ceiling suffice). Our experiments test the limits of this side channel with a novel signal processing pipeline that extracts and recognizes the leaked acoustic information. Our evaluation with 10 smartphones on a spoken digit dataset reports 80.66%, 91.28%, and 99.67% accuracies on recognizing 10 spoken digits, 20 speakers, and 2 genders respectively. We further systematically discuss the possible defense strategies and implementations. By modeling, measuring, and demonstrating the limits of acoustic eavesdropping from smartphone camera image streams, our contributions explain the physics-based causality and possible ways to reduce the threat on current and future devices.

Personal video conferencing has become a new norm after COVID-19 caused a seismic shift from in-person meetings and phone calls to video conferencing for daily communications and sensitive business. Video leaks participants’ on-screen information because eyeglasses and other reflective objects unwittingly expose partial screen contents. Using mathematical modeling and human subjects experiments, this research explores the extent to which emerging webcams might leak recognizable textual and graphical information gleaming from eyeglass reflections captured by webcams. The primary goal of our work is to measure, compute, and predict the factors, limits, and thresholds of recognizability as webcam technology evolves in the future. Our work explores and characterizes the viable threat models based on optical attacks using multi-frame super resolution techniques on sequences of video frames. Our models and experimental results in a controlled lab setting show it is possible to reconstruct and recognize with over 75% accuracy on-screen texts that have heights as small as 10 mm with a 720p webcam. We further apply this threat model to web textual contents with varying attacker capabilities to find thresholds at which text becomes recognizable. Our user study with 20 participants suggests present-day 720p webcams are sufficient for adversaries to reconstruct textual content on big-font websites. Our models further show that the evolution towards 4K cameras will tip the threshold of text leakage to reconstruction of most header texts on popular websites. Besides textual targets, a case study on recognizing a closed-world dataset of Alexa top 100 websites with 720p webcams shows a maximum recognition accuracy of 94% with 10 participants even without using machine-learning models. Our research proposes near-term mitigations including a software prototype that users can use to blur the eyeglass areas of their video streams. For possible long-term defenses, we advocate an individual reflection testing procedure to assess threats under various settings, and justify the importance of following the principle of least privilege for privacy-sensitive scenarios.

While the embedded security research community aims to protect systems by reducing analog sensor side channels, our work argues that sensor side channels can be beneficial to defenders. This work introduces the general problem of synthesizing virtual sensors from existing circuits to authenticate physical sensors’ measurands. We investigate how to apply this approach and present a preliminary analytical framework and definitions for sensors side channels. To illustrate the general concept, we provide a proof-of-concept case study to synthesize a virtual inertial measurement unit from a camera motion side channel. Our work also provides an example of applying this technique to protect facial recognition against silicon mask spoofing attacks. Finally, we discuss downstream problems of how to ensure that side channels benefit the defender, but not the adversary, during authentication.

Autonomous vehicles increasingly exploit computer-vision-based object detection systems to perceive environments and make critical driving decisions. To increase the quality of images, image stabilizers with inertial sensors are added to alleviate image blurring caused by camera jitters. However, such a trend opens a new attack surface. This paper identifies a system-level vulnerability resulting from the combination of the emerging image stabilizer hardware susceptible to acoustic manipulation and the object detection algorithms subject to adversarial examples. By emitting deliberately designed acoustic signals, an adversary can control the output of an inertial sensor, which triggers unnecessary motion compensation and results in a blurred image, even if the camera is stable. The blurred images can then induce object misclassification affecting safety-critical decision making. We model the feasibility of such acoustic manipulation and design an attack framework that can accomplish three types of attacks, i.e., hiding, creating, and altering objects. Evaluation results demonstrate the effectiveness of our attacks against four academic object detectors (YOLO V3/V4/V5 and Fast R-CNN), and one commercial detector (Apollo). We further introduce the concept of AMpLe attacks, a new class of system-level security vulnerabilities resulting from a combination of adversarial machine learning and physics-based injection of information-carrying signals into hardware.

In response to the N95 mask shortage caused by the COVID-19 pandemic, the US CDC has recognized moist-heat as one of the most effective and accessible methods for decontaminating N95 masks for reuse. However, it is challenging to reliably deploy this technique in healthcare settings due to a lack of specialized equipment capable of ensuring proper decontamination conditions. To this end, we developed a wireless sensor platform for moist-heat decontamination process verification, capable of monitoring hundreds of masks simultaneously in commercially available heating systems. Our easy-to-use, low-power, low-cost, scalable platform can be broadly deployed to protect front-line healthcare workers by lowering their risk of infection from reused N95 masks.

Over the last six years, several papers demonstrated how intentional analog interference based on acoustics, RF, lasers, and other physical modalities could induce faults, influence, or even control the output of sensors. Damage to the availability and integrity of sensor output carries significant risks to safety-critical systems that make automated decisions based on trusted sensor measurement. Established signal processing models use transfer functions to express reliability and dependability characteristics of sensors, but existing models do not provide a deliberate way to express and capture security properties meaningfully.Our work begins to fill this gap by systematizing knowledge of analog attacks against sensor circuitry and defenses. Our primary contribution is a simple sensor security model such that sensor engineers can better express analog security properties of sensor circuitry without needing to learn significantly new notation. Our model introduces transfer functions and a vector of adversarial noise to represent adversarial capabilities at each stage of a sensor's signal conditioning chain. The primary goals of the systematization are (1) to enable more meaningful quantification of risk for the design and evaluation of past and future sensors, (2) to better predict new attack vectors, and (3) to establish defensive design patterns that make sensors more resistant to analog attacks.

In Autonomous Vehicles (AVs), one fundamental pillar is perception,which leverages sensors like cameras and LiDARs (Light Detection and Ranging) to understand the driving environment. Due to its direct impact on road safety, multiple prior efforts have been made to study its the security of perception systems. In contrast to prior work that concentrates on camera-based perception, in this work we perform the first security study of LiDAR-based perception in AV settings, which is highly important but unexplored. We consider LiDAR spoofing attacks as the threat model and set the attack goal as spoofing obstacles close to the front of a victim AV. We find that blindly applying LiDAR spoofing is insufficient to achieve this goal due to the machine learning-based object detection process.Thus, we then explore the possibility of strategically controlling the spoofed attack to fool the machine learning model. We formulate this task as an optimization problem and design modeling methods for the input perturbation function and the objective function.We also identify the inherent limitations of directly solving the problem using optimization and design an algorithm that combines optimization and global sampling, which improves the attack success rates to around 75%. As a case study to understand the attack impact at the AV driving decision level, we construct and evaluate two attack scenarios that may damage road safety and mobility.We also discuss defense directions at the AV system, sensor, and machine learning model levels.

Temperature sensing and control systems are widely used in the closed-loop control of critical processes such as maintaining the thermal stability of patients, or in alarm systems for detecting temperature-related hazards. However, the security of these systems has yet to be completely explored, leaving potential attack surfaces that can be exploited to take control over critical systems.

Security conscious individuals may take considerable measures to disable sensors in order to protect their privacy. However, they often overlook the cyberphysical attack surface exposed by devices that were never designed to be sensors in the first place. Our research demonstrates that the mechanical components in magnetic hard disk drives behave as microphones with sufficient precision to extract and parse human speech. These unintentional microphones sense speech with high enough fidelity for the Shazam service to recognize a song recorded through the hard drive. This proof of concept attack sheds light on the possibility of invasion of privacy even in absence of traditional sensors. We also present defense mechanisms, such as the use of ultrasonic aliasing, that can mitigate acoustic eavesdropping by synthesized microphones in hard disk drives.

Intentional acoustic interference causes unusual errors in the mechanics of magnetic hard disk drives in desktop and laptop computers, leading to damage to integrity and availability in both hardware and software such as file system corruption and operating system reboots. An adversary without any special purpose equipment can co-opt built-in speakers or nearby emitters to cause persistent errors. Our work traces the deeper causality of these risks from the physics of materials to the I/O request stack in operating systems for audible and ultrasonic sound. Our experiments show that audible sound causes the head stack assembly to vibrate outside of operational bounds; ultrasonic sound causes false positives in the shock sensor, which is designed to prevent a head crash. The problem poses a challenge for legacy magnetic disks that remain stubbornly common in safety critical applications such as medical devices and other highly utilized systems difficult to sunset. Thus, we created and modeled a new feedback controller that could be deployed as a firmware update to attenuate the intentional acoustic interference. Our sensor fusion method prevents unnecessary head parking by detecting ultrasonic triggering of the shock sensor.

Cyber-physical systems depend on sensors to make automated decisions. Resonant acoustic injection attacks are already known to cause malfunctions by disabling MEMS-based gyroscopes. However, an open question remains on how to move beyond denial of service attacks to achieve full adversarial control of sensor outputs. Our work investigates how analog acoustic injection attacks can damage the digital integrity of a popular type of sensor: the capacitive MEMS accelerometer. Spoofing such sensors with intentional acoustic interference enables an out-of-spec pathway for attackers to deliver chosen digital values to microprocessors and embedded systems that blindly trust the unvalidated integrity of sensor outputs. Our contributions include (1) modeling the physics of malicious acoustic interference on MEMS accelerometers, (2) discovering the circuit-level security flaws that cause the vulnerabilities by measuring acoustic injection attacks on MEMS accelerometers as well as systems that employ on these sensors, and (3) two software-only defenses that mitigate many of the risks to the integrity of MEMS accelerometer outputs. We characterize two classes of acoustic injection attacks with increasing levels of adversarial control: output biasing and output control. We test these attacks against 20 models of capacitive MEMS accelerometers from 5 different manufacturers. Our experiments find that 75% are vulnerable to output biasing, and 65% are vulnerable to output control. To illustrate end-to-end implications, we show how to inject fake steps into a Fitbit with a $5 speaker. In our self-stimulating attack, we play a malicious music file from a smartphone's speaker to control the on-board MEMS accelerometer trusted by a local app to pilot a toy RC car. In addition to offering hardware design suggestions to eliminate the root causes of insecure amplification and filtering, we introduce two low-cost software defenses that mitigate output biasing attacks: randomized sampling and 180 degree out-of-phase sampling. These software-only approaches mitigate attacks by exploiting the periodic and predictable nature of the malicious acoustic interference signal. Our results call into question the wisdom of allowing microprocessors and embedded systems to blindly trust that hardware abstractions alone will ensure the integrity of sensor outputs.

Today, it would be difficult to find medical device technology that does not critically depend on computer software. Network connectivity and wireless communication has transformed the delivery of patient care. The technology often enables patients to lead more normal and healthy lives. However, medical devices that rely on software (e.g., drug infusion pumps, linear accelerators, pacemakers) also inherit the pesky cybersecurity risks endemic to computing. What's special about medical devices and cybersecurity? What's hype and what's real? What can history teach us? How are international standards bodies and regulatory cybersecurity requirements changing the global manufacture of medical devices? This talk will provide a glimpse into the risks, benefits, and regulatory issues for medical device cybersecurity and innovation of trustworthy medical device software.

Sensing platforms are becoming batteryless to enable the vision of the Internet of Things, where trillions of devices collect data, interact with each other, and interact with people. However, these batteryless sensing platforms—that rely purely on energy harvesting—are rarely able to maintain a sense of time after a power failure. This makes working with sensor data that is time sensitive especially difficult. We propose two novel, zero-power timekeepers that use remanence decay to measure the time elapsed between power failures. Our approaches compute the elapsed time from the amount of decay of a capacitive device, either on-chip Static Random-Access Memory (SRAM) or a dedicated capacitor. This enables hourglass-like timers that give intermittently powered sensing devices a persistent sense of time. Our evaluation shows that applications using either timekeeper can keep time accurately through power failures as long as 45s with low overhead.

Approximate computing research seeks to trade-off the accuracy of computation for increases in performance or reductions in power consumption. The observation driving approximate computing is that many applications tolerate small amounts of error which allows for an opportunistic relaxation of guard bands (e.g., clock rate and voltage). Besides affecting performance and power, reducing guard bands exposes analog properties of traditionally digital components. For DRAM, one analog property exposed by approximation is the variability of memory cell decay times.

Physical unclonable functions (PUFs) are circuits that produce outputs determined by random physical variations from fabrication. The PUF studied in this paper utilizes the variation sensitivity of static random access memory (SRAM) data retention voltage (DRV), the minimum voltage at which each cell can retain state. Prior work shows that DRV can uniquely identify circuit instances with 28% greater success than SRAM power-up states that are used in PUFs [1]. However, DRV is highly sensitive to temperature, and until now this makes it unreliable and unsuitable for use in a PUF. In this paper, we enable DRV PUFs by proposing a DRV-based hash function that is insensitive to temperature. The new hash function, denoted DRV-based hashing (DH), is reliable across temperatures because it utilizes the temperature-insensitive ordering of DRVs across cells, instead of using the DRVs in absolute terms. To evaluate the security and performance of the DRV PUF, we use DRV measurements from commercially available SRAM chips, and use data from a novel DRV prediction algorithm. The prediction algorithm uses machine learning for fast and accurate simulation-free estimation of any cell's DRV, and the prediction error in comparison to circuit simulation has a standard deviation of 0.35 mV. We demonstrate the DRV PUF using two applications-secret key generation and identification. In secret key generation, we introduce a new circuit-level reliability knob as an alternative to error correcting codes. In the identification application, our approach is compared to prior work and shown to result in a smaller false-positive identification rate for any desired true-positive identification rate.

Electromagnetic interference (EMI) affects circuits by inducing voltages on conductors. Analog sensing of signals on the order of a few millivolts is particularly sensitive to interference. This work (1) measures the susceptibility of analog sensor systems to signal injection attacks by intentional, low-power emission of chosen electromagnetic waveforms, and (2) proposes defense mechanisms to reduce the risks. Our experiments use specially crafted EMI at varying power and distance to measure susceptibility of sensors in implantable medical devices and consumer electronics. Results show that at distances of 1-2m, consumer electronic devices containing microphones are vulnerable to the injection of bogus audio signals. Our measurements show that in free air, intentional EMI under 10 W can inhibit pacing and induce defibrillation shocks at distances up to 1-2m on implantable cardiac electronic devices. However, with the sensing leads and medical devices immersed in a saline bath to better approximate the human body, the same experiment decreases to about 5 cm. Our defenses range from prevention with simple analog shielding to detection with a signal contamination metric based on the root mean square of waveform amplitudes. Our contribution to securing cardiac devices includes a novel defense mechanism that probes for forged pacing pulses inconsistent with the refractory period of cardiac tissue.

This work analyzes the stochastic behavior of writing to embedded flash memory at voltages lower than recommended by a microcontroller’s specifications in order to reduce energy consumption. Flash memory integrated within a microcontroller typically requires the entire chip to operate on a common supply voltage almost twice as much as what the CPU portion requires. Our software approach allows the flash memory to tolerate a lower supply voltage so that the CPU may operate in a more energy-efficient manner. Energy-efficient coding algorithms then cope with flash memory writes that behave unpredictably. Our software-only coding algorithms ( in-place writes, multiple-place writes, RS-Berger codes , and slow writes ) enable reliable storage at low voltages on unmodified hardware by exploiting the electrically cumulative nature of half-written data in write-once bits. For a sensor monitoring application using the MSP430, coding with in-place writes reduces the overall energy consumption by 34%. In-place writes are competitive when the time spent on low-voltage operations such as computation are at least four times greater than the time spent on writes to flash memory. Our evaluation shows that tightly maintaining the digital abstraction for storage in embedded flash memory comes at a significant cost to energy consumption with minimal gain in reliability. We find our techniques most effective for embedded workloads that have significant duty cycling, rare writes, or energy harvesting.

Implantable medical devices, or IMDs, are increasingly being used to improve patients' medical outcomes. Designers of IMDs already balance safety, reliability, complexity, power consumption, and cost. However, recent research has demonstrated that designers should also consider security and data privacy to protect patients from acts of theft or malice, especially as medical technology becomes increasingly connected to other systems via wireless communications or the Internet. This survey paper summarizes recent work on IMD security. It discusses sound security principles to follow and common security pitfalls to avoid. As trends in power efficiency, sensing, wireless systems and bio-interfaces make possible new and improved IMDs, they also underscore the importance of understanding and addressing security and privacy concerns in an increasingly connected world.

Transiently powered computing devices such as RFID tags, kinetic energy harvesters, and smart cards typically rely on programs that complete a task under tight time constraints before energy starvation leads to complete loss of volatile memory. Mementos is a software system that transforms general-purpose programs into interruptible computations that are protected from frequent power losses by automatic, energy-aware state checkpointing. Mementos comprises a collection of optimization passes for the LLVM compiler infrastructure and a linkable library that exercises hardware support for energy measurement while managing state checkpoints stored in nonvolatile memory. We evaluate Mementos against diverse test cases in a trace-driven simulator of transiently powered RFID-scale devices. Although Mementos's energy checks increase run time when energy is plentiful, they allow Mementos to safely suspend execution when energy dwindles, effectively spreading computation across zero or more power failures. This paper's contributions are: a study of the runtime environment for programs on RFID-scale devices; an energy-aware state checkpointing system for these devices that is implemented for the MSP430 family of microcontrollers; and a trace-driven simulator of transiently powered RFID-scale devices.

Wireless communication has become an intrinsic part of modern implantable medical devices (IMDs). Recent work, however, has demonstrated that wireless connectivity can be exploited to compromise the confidentiality of IMDs' transmitted data or to send unauthorized commands to IMDs---even commands that cause the device to deliver an electric shock to the patient. The key challenge in addressing these attacks stems from the difficulty of modifying or replacing already-implanted IMDs. Thus, in this paper, we explore the feasibility of protecting an implantable device from such attacks without modifying the device itself. We present a physical-layer solution that delegates the security of an IMD to a personal base station called the shield. The shield uses a novel radio design that can act as a jammer-cum-receiver. This design allows it to jam the IMD's messages, preventing others from decoding them while being able to decode them itself. It also allows the shield to jam unauthorized commands---even those that try to alter the shield's own transmissions. We implement our design in a software radio and evaluate it with commercial IMDs. We find that it effectively provides confidentiality for private data and protects the IMD from unauthorized commands.

Mobile sensing is difficult without power. Emerging Computational RFIDs (CRFIDs) provide both sensing and general-purpose computation without batteries--instead relying on small capacitors charged by energy harvesting. CRFIDs have small form factors and consume less energy than traditional sensor motes. However, CRFIDs have yet to see widespread use because of limited autonomy and the propensity for frequent power loss as a result of the necessarily small capacitors that serve as a microcontroller's power supply. Our results show that hybrid harvesting CRFIDs, which use an ambient energy micro-harvester, can complete a variety of useful workloads--even in an environment with little ambient energy available.

RFID sensor networks comprising batteryless devices that are passively powered by RFID readers present exciting possibilities for ubiquitous computing applications. They require minimal maintenance, are cheap to manufacture and have small form factor. However, their lack of autonomy due to the need for constant power from an RFID reader hinders their deployment. We demonstrate that RFIDs augmented with ambient energy-harvesting capabilities may be used as a first-class sensor platform, allowing them to operate untethered from reader infrastructure. Specifically, we show that a CRFID-based accelerometer sensor can provide both real-time and delayed access to time-stamped sensor data when provided with a small amount of solar energy. The data is collected using a standard RFID reader and displayed in a graphical interface.

Intermittently powered applications create a need for low-cost security and privacy in potentially hostile environments, supported by primitives including identification and random number generation. Our measurements show that power-up of SRAM produces a physical fingerprint. We propose a system of fingerprint extraction and random numbers in SRAM (FERNS) that harvests static identity and randomness from existing volatile CMOS memory without requiring any dedicated circuitry. The identity results from manufacture-time physically random device threshold voltage mismatch, and the random numbers result from runtime physically random noise. We use experimental data from high-performance SRAM chips and the embedded SRAM of the WISP UHF RFID tag to validate the principles behind FERNS. For the SRAM chip, we demonstrate that 8-byte fingerprints can uniquely identify circuits among a population of 5,120 instances and extrapolate that 24-byte fingerprints would uniquely identify all instances ever produced. Using a smaller population, we demonstrate similar identifying ability from the embedded SRAM. In addition to identification, we show that SRAM fingerprints capture noise, enabling true random number generation. We demonstrate that a 512-byte SRAM fingerprint contains sufficient entropy to generate 128-bit true random numbers and that the generated numbers pass the NIST tests for runs, approximate entropy, and block frequency.

Our study analyzes the security and privacy properties of an implantable cardioverter defibrillator (ICD). Introduced to the U.S. market in 2003, this model of ICD includes pacemaker technology and is designed to communicate wirelessly with a nearby external programmer in the 175 kHz frequency range. After partially reverse-engineering the ICD's communications protocol with an oscilloscope and a software radio, we implemented several software radio-based attacks that could compromise patient safety and patient privacy. Motivated by our desire to improve patient safety, and mindful of conventional trade-offs between security and power consumption for resource-constrained devices, we introduce three new zero-power defenses based on RF power harvesting. Two of these defenses are human-centric, bringing patients into the loop with respect to the security and privacy of their implantable medical devices (IMDs). Our contributions provide a scientific baseline for understanding the potential security and privacy risks of current and future IMDs, and introduce human-perceptible and zero-power mitigation techniques that address those risks. To the best of our knowledge, this paper is the first in our community to use general-purpose software radios to analyze and attack previously unknown radio communications protocols.

In 1998, Blaze, Bleumer, and Strauss (BBS) proposed an application called atomic proxy re-encryption , in which a semitrusted proxy converts a ciphertext for Alice into a ciphertext for Bob without seeing the underlying plaintext. We predict that fast and secure re-encryption will become increasingly popular as a method for managing encrypted file systems. Although efficiently computable, the wide-spread adoption of BBS re-encryption has been hindered by considerable security risks. Following recent work of Dodis and Ivan, we present new re-encryption schemes that realize a stronger notion of security and demonstrate the usefulness of proxy re-encryption as a method of adding access control to a secure file system. Performance measurements of our experimental file system demonstrate that proxy re-encryption can work effectively in practice.

Internet users increasingly rely on publicly available data for everything from software installation to investment decisions. Unfortunately, the vast majority of public content on the Internet comes with no integrity or authenticity guarantees. This paper presents the self-certifying read-only file system, a content distribution system providing secure, scalable access to public, read-only data.The read-only file system makes the security of published content independent from that of the distribution infrastructure. In a secure area (perhaps off-line), a publisher creates a digitally signed database out of a file system's contents. The publisher then replicates the database on untrusted content-distribution servers, allowing for high availability.The read-only file system avoids performing any cryptographic operations on servers and keeps the overhead of cryptography low on clients, allowing servers to scale to a large number of clients. Measurements of an implementation show that an individual server running on a 550-Mhz Pentium III with FreeBSD can support 1,012 connections per second and 300 concurrent clients compiling a large software package.