78,358 research outputs found
Secure Stream Processing for Medical Data
Medical data belongs to whom it produces it. In an increasing manner, this
data is usually processed in unauthorized third-party clouds that should never
have the opportunity to access it. Moreover, recent data protection regulations
(e.g., GDPR) pave the way towards the development of privacy-preserving
processing techniques. In this paper, we present a proof of concept of a
streaming IoT architecture that securely processes cardiac data in the cloud
combining trusted hardware and Spark. The additional security guarantees come
with no changes to the application's code in the server. We tested the system
with a database containing ECGs from wearable devices comprised of 8 healthy
males performing a standarized range of in-lab physisical activities (e.g.,
run, walk, bike). We show that, when compared with standard Spark Streaming,
the addition of privacy comes at the cost of doubling the execution time
Using Trusted Execution Environments for Secure Stream Processing of Medical Data
Processing sensitive data, such as those produced by body sensors, on
third-party untrusted clouds is particularly challenging without compromising
the privacy of the users generating it. Typically, these sensors generate large
quantities of continuous data in a streaming fashion. Such vast amount of data
must be processed efficiently and securely, even under strong adversarial
models. The recent introduction in the mass-market of consumer-grade processors
with Trusted Execution Environments (TEEs), such as Intel SGX, paves the way to
implement solutions that overcome less flexible approaches, such as those atop
homomorphic encryption. We present a secure streaming processing system built
on top of Intel SGX to showcase the viability of this approach with a system
specifically fitted for medical data. We design and fully implement a prototype
system that we evaluate with several realistic datasets. Our experimental
results show that the proposed system achieves modest overhead compared to
vanilla Spark while offering additional protection guarantees under powerful
attackers and threat models.Comment: 19th International Conference on Distributed Applications and
Interoperable System
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A smartphone-based Teleradiology system
The development of a teleradiology application for remote monitoring and processing of patient image data using 2nd generation mobile devices with enhanced network services, is of extreme interest, especially when the final means of display is a smartphone, a very light and compact handheld device. In the following paper the development of applications, that are responsible for remote monitoring and processing of medical images, is investigated
Medical data processing and analysis for remote health and activities monitoring
Recent developments in sensor technology, wearable computing, Internet of Things (IoT), and wireless communication have given rise to research in ubiquitous healthcare and remote monitoring of human\u2019s health and activities. Health monitoring systems involve processing and analysis of data retrieved from smartphones, smart watches, smart bracelets, as well as various sensors and wearable devices. Such systems enable continuous monitoring of patients psychological and health conditions by sensing and transmitting measurements such as heart rate, electrocardiogram, body temperature, respiratory rate, chest sounds, or blood pressure. Pervasive healthcare, as a relevant application domain in this context, aims at revolutionizing the delivery of medical services through a medical assistive environment and facilitates the independent living of patients. In this chapter, we discuss (1) data collection, fusion, ownership and privacy issues; (2) models, technologies and solutions for medical data processing and analysis; (3) big medical data analytics for remote health monitoring; (4) research challenges and opportunities in medical data analytics; (5) examples of case studies and practical solutions
A Novel Latin Square Image Cipher
In this paper, we introduce a symmetric-key Latin square image cipher (LSIC)
for grayscale and color images. Our contributions to the image encryption
community include 1) we develop new Latin square image encryption primitives
including Latin Square Whitening, Latin Square S-box and Latin Square P-box ;
2) we provide a new way of integrating probabilistic encryption in image
encryption by embedding random noise in the least significant image bit-plane;
and 3) we construct LSIC with these Latin square image encryption primitives
all on one keyed Latin square in a new loom-like substitution-permutation
network. Consequently, the proposed LSIC achieve many desired properties of a
secure cipher including a large key space, high key sensitivities, uniformly
distributed ciphertext, excellent confusion and diffusion properties,
semantically secure, and robustness against channel noise. Theoretical analysis
show that the LSIC has good resistance to many attack models including
brute-force attacks, ciphertext-only attacks, known-plaintext attacks and
chosen-plaintext attacks. Experimental analysis under extensive simulation
results using the complete USC-SIPI Miscellaneous image dataset demonstrate
that LSIC outperforms or reach state of the art suggested by many peer
algorithms. All these analysis and results demonstrate that the LSIC is very
suitable for digital image encryption. Finally, we open source the LSIC MATLAB
code under webpage https://sites.google.com/site/tuftsyuewu/source-code.Comment: 26 pages, 17 figures, and 7 table
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