e-SAFE: Secure, Efficient and Forensics-Enabled Access to Implantable Medical Devices

To facilitate monitoring and management, modern Implantable Medical Devices (IMDs) are often equipped with wireless capabilities, which raise the risk of malicious access to IMDs. Although schemes are proposed to secure the IMD access, some issues are still open. First, pre-sharing a long-term key between a patient's IMD and a doctor's programmer is vulnerable since once the doctor's programmer is compromised, all of her patients suffer; establishing a temporary key by leveraging proximity gets rid of pre-shared keys, but as the approach lacks real authentication, it can be exploited by nearby adversaries or through man-in-the-middle attacks. Second, while prolonging the lifetime of IMDs is one of the most important design goals, few schemes explore to lower the communication and computation overhead all at once. Finally, how to safely record the commands issued by doctors for the purpose of forensics, which can be the last measure to protect the patients' rights, is commonly omitted in the existing literature. Motivated by these important yet open problems, we propose an innovative scheme e-SAFE, which significantly improves security and safety, reduces the communication overhead and enables IMD-access forensics. We present a novel lightweight compressive sensing based encryption algorithm to encrypt and compress the IMD data simultaneously, reducing the data transmission overhead by over 50% while ensuring high data confidentiality and usability. Furthermore, we provide a suite of protocols regarding device pairing, dual-factor authentication, and accountability-enabled access. The security analysis and performance evaluation show the validity and efficiency of the proposed scheme

The SURPRISE demonstrator: a super-resolved compressive instrument in the visible and medium infrared for Earth Observation

While Earth Observation (EO) data has become ever more vital to understanding the planet and addressing societal challenges, applications are still limited by revisit time and spatial resolution. Though low Earth orbit missions can achieve resolutions better than 100 m, their revisit time typically stands at several days, limiting capacity to monitor dynamic events. Geostationary (GEO) missions instead typically provide data on an hour-basis but with spatial resolution limited to 1 km, which is insufficient to understand local phenomena. In this paper, we present the SURPRISE project - recently funded in the frame of the H2020 programme – that gathers the expertise from eight partners across Europe to implement a demonstrator of a super-spectral EO payload - working in the visible (VIS) - Near Infrared (NIR) and in the Medium InfraRed (MIR) and conceived to operate from GEO platform -with enhanced performance in terms of at-ground spatial resolution, and featuring innovative on-board data processing and encryption functionalities. This goal will be achieved by using Compressive Sensing (CS) technology implemented via Spatial Light Modulators (SLM). SLM-based CS technology will be used to devise a super-resolution configuration that will be exploited to increase the at-ground spatial resolution of the payload, without increasing the number of detector’s sensing elements at the image plane. The CS approach will offer further advantages for handling large amounts of data, as is the case of superspectral payloads with wide spectral and spatial coverage. It will enable fast on-board processing of acquired data for information extraction, as well as native data encryption on top of native compression. SURPRISE develops two disruptive technologies: Compressive Sensing (CS) and Spatial Light Modulator (SLM). CS optimises data acquisition (e.g. reduced storage and transmission bandwidth requirements) and enables novel onboard processing and encryption functionalities. SLM here implements the CS paradigm and achieves a super-resolution architecture. SLM technology, at the core of the CS architecture, is addressed by: reworking and testing off-the-shelf parts in relevant environment; developing roadmap for a European SLM, micromirror array-type, with electronics suitable for space qualification. By introducing for the first time the concept of a payload with medium spatial resolution (few hundreds of meters) and near continuous revisit (hourly), SURPRISE can lead to a EO major breakthrough and complement existing operational services. CS will address the challenge of large data collection, whilst onboard processing will improve timeliness, shortening time needed to extract information from images and possibly generate alarms. Impact is relevant to industrial competitiveness, with potential for market penetration of the demonstrator and its components

Roadmap on optical security

Postprint (author's final draft

IoT-Based Multi-Dimensional Chaos Mapping System for Secure and Fast Transmission of Visual Data in Smart Cities

A “smart city” sends data from many sensors to a cloud server for local authorities and the public to connect. Smart city residents communicate mostly through images and videos. Many image security algorithms have been proposed to improve locals’ lives, but a high-class redundancy method with a small space requirement is still needed to acquire and protect this sensitive data. This paper proposes an IoT-based multi-dimensional chaos mapping system for secure and fast transmission of visual data in smart cities, which uses the five dimensional Gauss Sine Logistic system to generate hyper-chaotic sequences to encrypt images. The proposed method also uses pixel position permutation and Singular Value Decomposition with Discrete fractional cosine transform to compress and protect the sensitive image data. To increase security, we use a chaotic system to construct the chaotic sequences and a diffusion matrix. Furthermore, numerical simulation results and theoretical evaluations validate the suggested scheme’s security and efficacy after compression encryption.publishedVersio

Low-complexity Multiclass Encryption by Compressed Sensing

The idea that compressed sensing may be used to encrypt information from unauthorised receivers has already been envisioned, but never explored in depth since its security may seem compromised by the linearity of its encoding process. In this paper we apply this simple encoding to define a general private-key encryption scheme in which a transmitter distributes the same encoded measurements to receivers of different classes, which are provided partially corrupted encoding matrices and are thus allowed to decode the acquired signal at provably different levels of recovery quality. The security properties of this scheme are thoroughly analysed: firstly, the properties of our multiclass encryption are theoretically investigated by deriving performance bounds on the recovery quality attained by lower-class receivers with respect to high-class ones. Then we perform a statistical analysis of the measurements to show that, although not perfectly secure, compressed sensing grants some level of security that comes at almost-zero cost and thus may benefit resource-limited applications. In addition to this we report some exemplary applications of multiclass encryption by compressed sensing of speech signals, electrocardiographic tracks and images, in which quality degradation is quantified as the impossibility of some feature extraction algorithms to obtain sensitive information from suitably degraded signal recoveries.Comment: IEEE Transactions on Signal Processing, accepted for publication. Article in pres