A Novel Method for Privacy Preservation of Health Data Stream

The power of human vision to synthesize information and recognize pattern is fundamental to the success of visualization as a scientific method. This same power can mislead investigators who use visualization to explore georeferenced data—if data reliability is not addressed directly in the visualization process. Here, we apply an integrated cognitive-semiotic approach to devise and test three methods for depicting reliability of georeferenced health data. The first method makes use of adjacent maps, one for data and one for reliability. This form of paired representation is compared to two methods in which data and reliability are spatially coincident (on a single map). A novel method for coincident visually separable depiction of data and data reliability on mortality maps (using a color fill to represent data and a texture overlay to represent reliability) is found to be effective in allowing map users to recognize unreliable data without interfering with their ability to notice clusters and characterize patterns in mortality rates. A coincident visually integral depiction (using color characteristics to represent both data and reliability) is found to inhibit perception of clusters that contain some enumeration units with unreliable data, and to make it difficult for users to consider data and reliability independently.

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Extremely Randomized Trees with Privacy Preservation for Distributed Structured Health Data

IEEE Access ◽

10.1109/access.2022.3141709 ◽

2022 ◽

pp. 1-1

Author(s):

Amin Aminifar ◽

Matin Shokri ◽

Fazle Rabbi ◽

Violet Ka I Pun ◽

Yngve Lamo

Keyword(s):

Privacy Preservation ◽

Health Data ◽

Extremely Randomized Trees

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Cloud Based Reasoning Health Data using Homomorphic Encryption

International Journal of Recent Technology and Engineering - 2 ◽

10.35940/ijrte.f9263.059120 ◽

2020 ◽

Vol 9 (1) ◽

pp. 609-611

Keyword(s):

Privacy Preservation ◽

Service Providers ◽

Homomorphic Encryption ◽

Health Data ◽

Insider Threat ◽

Due Diligence ◽

Encrypted Data ◽

Plain Text ◽

Security Issues ◽

Client Data

Cloud computing is an abundant heterogeneous paradigm. The clients are given access to cloud for storing large amount of data for many purposes. The major cloud security issues are data breaches, insider threat and insufficient due diligence etc. Most of the service providers save the Client data as a plain text format which makes the data less secured. Aim of the system is to protect the health data that are outsourced for storing in cloud. In this system, the data is encrypted using paillier cryptosystem before outsourcing, which preserves the privacy of patient’s health data. Computations are performed over this encrypted data using decision tree algorithm. The results are displayed on the client machine. Hence, it ensures the privacy preservation and cautions the patient about his health.

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Fully unsupervised online spike sorting based on an artificial spiking neural network

10.1101/236224 ◽

2017 ◽

Cited By ~ 1

Author(s):

Marie Bernert ◽

Blaise Yvert

Keyword(s):

Neural Network ◽

Data Stream ◽

Implantable Devices ◽

Spiking Neural Network ◽

Spike Sorting ◽

Neural Data ◽

Low Snr ◽

Novel Method ◽

Fully Automatic ◽

Analog Systems

AbstractSpike sorting is a crucial step of neural data processing widely used in neuroscience and neuroprosthetics. However, current methods remain not fully automatic and require heavy computations making them not embeddable in implantable devices. To overcome these limitations, we propose a novel method based on an artificial spiking neural network designed to process neural data online and completely automatically. An input layer continuously encodes the data stream into artificial spike trains, which are then processed by two further layers to output artificial trains of spikes reproducing the real spiking activity present in the input signal. The proposed method can be adapted to process several channels simultaneously in the case of tetrode recordings. It outperforms two existing algorithms at low SNR and has the advantage to be compatible with neuromorphic computing and the perspective of being embedded in very low-power analog systems for future implantable devices serving neurorehabilitation applications.

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