1,373 research outputs found
Roadmap on optical security
Postprint (author's final draft
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
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
On Known-Plaintext Attacks to a Compressed Sensing-based Encryption: A Quantitative Analysis
Despite the linearity of its encoding, compressed sensing may be used to
provide a limited form of data protection when random encoding matrices are
used to produce sets of low-dimensional measurements (ciphertexts). In this
paper we quantify by theoretical means the resistance of the least complex form
of this kind of encoding against known-plaintext attacks. For both standard
compressed sensing with antipodal random matrices and recent multiclass
encryption schemes based on it, we show how the number of candidate encoding
matrices that match a typical plaintext-ciphertext pair is so large that the
search for the true encoding matrix inconclusive. Such results on the practical
ineffectiveness of known-plaintext attacks underlie the fact that even
closely-related signal recovery under encoding matrix uncertainty is doomed to
fail.
Practical attacks are then exemplified by applying compressed sensing with
antipodal random matrices as a multiclass encryption scheme to signals such as
images and electrocardiographic tracks, showing that the extracted information
on the true encoding matrix from a plaintext-ciphertext pair leads to no
significant signal recovery quality increase. This theoretical and empirical
evidence clarifies that, although not perfectly secure, both standard
compressed sensing and multiclass encryption schemes feature a noteworthy level
of security against known-plaintext attacks, therefore increasing its appeal as
a negligible-cost encryption method for resource-limited sensing applications.Comment: IEEE Transactions on Information Forensics and Security, accepted for
publication. Article in pres
Overview of compressed sensing: Sensing model, reconstruction algorithm, and its applications
With the development of intelligent networks such as the Internet of Things, network scales are becoming increasingly larger, and network environments increasingly complex, which brings a great challenge to network communication. The issues of energy-saving, transmission efficiency, and security were gradually highlighted. Compressed sensing (CS) helps to simultaneously solve those three problems in the communication of intelligent networks. In CS, fewer samples are required to reconstruct sparse or compressible signals, which breaks the restrict condition of a traditional Nyquist-Shannon sampling theorem. Here, we give an overview of recent CS studies, along the issues of sensing models, reconstruction algorithms, and their applications. First, we introduce several common sensing methods for CS, like sparse dictionary sensing, block-compressed sensing, and chaotic compressed sensing. We also present several state-of-the-art reconstruction algorithms of CS, including the convex optimization, greedy, and Bayesian algorithms. Lastly, we offer recommendation for broad CS applications, such as data compression, image processing, cryptography, and the reconstruction of complex networks. We discuss works related to CS technology and some CS essentials. © 2020 by the authors
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