21,113 research outputs found
Securing NextG networks with physical-layer key generation: A survey
As the development of next-generation (NextG) communication networks continues, tremendous devices are accessing the network and the amount of information is exploding. However, with the increase of sensitive data that requires confidentiality to be transmitted and stored in the network, wireless network security risks are further amplified. Physical-layer key generation (PKG) has received extensive attention in security research due to its solid information-theoretic security proof, ease of implementation, and low cost. Nevertheless, the applications of PKG in the NextG networks are still in the preliminary exploration stage. Therefore, we survey existing research and discuss (1) the performance advantages of PKG compared to cryptography schemes, (2) the principles and processes of PKG, as well as research progresses in previous network environments, and (3) new application scenarios and development potential for PKG in NextG communication networks, particularly analyzing the effect and prospects of PKG in massive multiple-input multiple-output (MIMO), reconfigurable intelligent surfaces (RISs), artificial intelligence (AI) enabled networks, integrated space-air-ground network, and quantum communication. Moreover, we summarize open issues and provide new insights into the development trends of PKG in NextG networks
UMSL Bulletin 2023-2024
The 2023-2024 Bulletin and Course Catalog for the University of Missouri St. Louis.https://irl.umsl.edu/bulletin/1088/thumbnail.jp
High-speed photon correlation monitoring of amplified quantum noise by chaos using deep-learning balanced homodyne detection
Precision experimental determination of photon correlation requires the
massive amounts of data and extensive measurement time. We present a technique
to monitor second-order photon correlation of amplified quantum
noise based on wideband balanced homodyne detection and deep-learning
acceleration. The quantum noise is effectively amplified by an injection of
weak chaotic laser and the of the amplified quantum noise is
measured with a real-time sample rate of 1.4 GHz. We also exploit a photon
correlation convolutional neural network accelerating correlation data using a
few quadrature fluctuations to perform a parallel processing of the
for various chaos injection intensities and effective bandwidths.
The deep-learning method accelerates the experimental acquisition
with a high accuracy, estimating 6107 sets of photon correlation data with a
mean square error of 0.002 in 22 seconds and achieving a three orders of
magnitude acceleration in data acquisition time. This technique contributes to
a high-speed and precision coherence evaluation of entropy source in secure
communication and quantum imaging.Comment: 6 pages, 6 figure
Encrypted Dynamic Control exploiting Limited Number of Multiplications and a Method using Ring-LWE based Cryptosystem
In this paper, we present a method to encrypt dynamic controllers that can be
implemented through most homomorphic encryption schemes, including somewhat,
leveled fully, and fully homomorphic encryption. To this end, we represent the
output of the given controller as a linear combination of a fixed number of
previous inputs and outputs. As a result, the encrypted controller involves
only a limited number of homomorphic multiplications on every encrypted data,
assuming that the output is re-encrypted and transmitted back from the
actuator. A guidance for parameter choice is also provided, ensuring that the
encrypted controller achieves predefined performance for an infinite time
horizon. Furthermore, we propose a customization of the method for
Ring-Learning With Errors (Ring-LWE) based cryptosystems, where a vector of
messages can be encrypted into a single ciphertext and operated simultaneously,
thus reducing computation and communication loads. Unlike previous results, the
proposed customization does not require extra algorithms such as rotation,
other than basic addition and multiplication. Simulation results demonstrate
the effectiveness of the proposed method.Comment: 11 pages, 4 figures, submitted to IEEE Transactions on Systems, Man,
and Cybernetics: System
Towards Fast and Scalable Private Inference
Privacy and security have rapidly emerged as first order design constraints.
Users now demand more protection over who can see their data (confidentiality)
as well as how it is used (control). Here, existing cryptographic techniques
for security fall short: they secure data when stored or communicated but must
decrypt it for computation. Fortunately, a new paradigm of computing exists,
which we refer to as privacy-preserving computation (PPC). Emerging PPC
technologies can be leveraged for secure outsourced computation or to enable
two parties to compute without revealing either users' secret data. Despite
their phenomenal potential to revolutionize user protection in the digital age,
the realization has been limited due to exorbitant computational,
communication, and storage overheads.
This paper reviews recent efforts on addressing various PPC overheads using
private inference (PI) in neural network as a motivating application. First,
the problem and various technologies, including homomorphic encryption (HE),
secret sharing (SS), garbled circuits (GCs), and oblivious transfer (OT), are
introduced. Next, a characterization of their overheads when used to implement
PI is covered. The characterization motivates the need for both GCs and HE
accelerators. Then two solutions are presented: HAAC for accelerating GCs and
RPU for accelerating HE. To conclude, results and effects are shown with a
discussion on what future work is needed to overcome the remaining overheads of
PI.Comment: Appear in the 20th ACM International Conference on Computing
Frontier
Root Extraction in Finite Abelian Groups
We formulate the Root Extraction problem in finite Abelian -groups and
then extend it to generic finite Abelian groups. We provide algorithms to solve
them. We also give the bounds on the number of group operations required for
these algorithms. We observe that once a basis is computed and the discrete
logarithm relative to the basis is solved, root extraction takes relatively
fewer "bookkeeping" steps. Thus, we conclude that root extraction in finite
Abelian groups is no harder than solving discrete logarithms and computing
basis
ACE: A Consent-Embedded privacy-preserving search on genomic database
In this paper, we introduce ACE, a consent-embedded searchable encryption
scheme. ACE enables dynamic consent management by supporting the physical
deletion of associated data at the time of consent revocation. This ensures
instant real deletion of data, aligning with privacy regulations and preserving
individuals' rights. We evaluate ACE in the context of genomic databases,
demonstrating its ability to perform the addition and deletion of genomic
records and related information based on ID, which especially complies with the
requirements of deleting information of a particular data owner. To formally
prove that ACE is secure under non-adaptive attacks, we present two new
definitions of forward and backward privacy. We also define a new hard problem,
which we call D-ACE, that facilitates the proof of our theorem (we formally
prove its hardness by a security reduction from DDH to D-ACE). We finally
present implementation results to evaluate the performance of ACE
Augmented Symbolic Execution for Information Flow in Hardware Designs
We present SEIF, a methodology that combines static analysis with symbolic
execution to verify and explicate information flow paths in a hardware design.
SEIF begins with a statically built model of the information flow through a
design and uses guided symbolic execution to recognize and eliminate non-flows
with high precision or to find corresponding paths through the design state for
true flows. We evaluate SEIF on two open-source CPUs, an AES core, and the AKER
access control module. SEIF can exhaustively explore 10-12 clock cycles deep in
4-6 seconds on average, and can automatically account for 86-90% of the paths
in the statically built model. Additionally, SEIF can be used to find multiple
violating paths for security properties, providing a new angle for security
verification
Seamless Multimodal Biometrics for Continuous Personalised Wellbeing Monitoring
Artificially intelligent perception is increasingly present in the lives of
every one of us. Vehicles are no exception, (...) In the near future, pattern
recognition will have an even stronger role in vehicles, as self-driving cars
will require automated ways to understand what is happening around (and within)
them and act accordingly. (...) This doctoral work focused on advancing
in-vehicle sensing through the research of novel computer vision and pattern
recognition methodologies for both biometrics and wellbeing monitoring. The
main focus has been on electrocardiogram (ECG) biometrics, a trait well-known
for its potential for seamless driver monitoring. Major efforts were devoted to
achieving improved performance in identification and identity verification in
off-the-person scenarios, well-known for increased noise and variability. Here,
end-to-end deep learning ECG biometric solutions were proposed and important
topics were addressed such as cross-database and long-term performance,
waveform relevance through explainability, and interlead conversion. Face
biometrics, a natural complement to the ECG in seamless unconstrained
scenarios, was also studied in this work. The open challenges of masked face
recognition and interpretability in biometrics were tackled in an effort to
evolve towards algorithms that are more transparent, trustworthy, and robust to
significant occlusions. Within the topic of wellbeing monitoring, improved
solutions to multimodal emotion recognition in groups of people and
activity/violence recognition in in-vehicle scenarios were proposed. At last,
we also proposed a novel way to learn template security within end-to-end
models, dismissing additional separate encryption processes, and a
self-supervised learning approach tailored to sequential data, in order to
ensure data security and optimal performance. (...)Comment: Doctoral thesis presented and approved on the 21st of December 2022
to the University of Port
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