Privacy-preserving image search (PPIS): Secure classification and searching using convolutional neural network over large-scale encrypted medical images

Guo, Cheng; Jia, Jing; Choo, Kim‐Kwang Raymond; Jie, Yingmo

doi:10.1016/j.cose.2020.102021

Cited by 26 publications

(19 citation statements)

References 12 publications

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“…This indicates that our scheme can distinguish the image's type of disease with high accuracy. In addition, we selected Securing SIFT, 21 MiniONN 14 and PPIS 15 to compare the classification accuracy with our scheme. We first trained the model and deployed them respectively and then randomly selected images from the medical image data set to test.…”

Section: Discussionmentioning

confidence: 99%

Privacy‐preserving CNN feature extraction and retrieval over medical images

Cai

Wei

Yao

2022

Int J of Intelligent Sys

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Section: Discussionmentioning

confidence: 99%

Privacy‐preserving CNN feature extraction and retrieval over medical images

Cai

Wei

Yao

2022

Int J of Intelligent Sys

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“…In Internet of medical things and healthcare systems, the shared and uploaded medical data are usually encrypted to protect the patient's data privacy in medical data. In [173], the authors proposed a data-search method based on encrypted images; this method improves the data-search accuracy while preserving data privacy.…”

Section: ) Image Processing and Computer Vision (Cv)mentioning

confidence: 99%

Compacting Deep Neural Networks for Internet of Things: Methods and Applications

Zhang

Ying

Dai

et al. 2021

IEEE Internet Things J.

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Deep Neural Networks (DNNs) have shown great success in completing complex tasks. However, DNNs inevitably bring high computational cost and storage consumption due to the complexity of hierarchical structures, thereby hindering their wide deployment in Internet-of-Things (IoT) devices, which have limited computational capability and storage capacity. Therefore, it is a necessity to investigate the technologies to compact DNNs. Despite tremendous advances in compacting DNNs, few surveys summarize compacting-DNNs technologies, especially for IoT applications. Hence, this paper presents a comprehensive study on compacting-DNNs technologies. We categorize compacting-DNNs technologies into three major types: 1) network model compression, 2) Knowledge Distillation (KD), 3) modification of network structures. We also elaborate on the diversity of these approaches and make side-by-side comparisons. Moreover, we discuss the applications of compacted DNNs in various IoT applications and outline future directions.

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“…Guo et al [104] Images Proposed a method based on the principles of artificial intelligence for securing medical images from adversaries. Encryption Liu et al [105] Traces Accurately predicted the travel time with privacy-preserving using the geo-indistinguishably sensitization and traces data.…”

Section: Encryptionmentioning

confidence: 99%

A Comprehensive Analysis of Privacy Protection Techniques Developed for COVID-19 Pandemic

Majeed

Hwang

2021

IEEE Access

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Since the emergence of coronavirus disease-2019 (COVID-19) outbreak, every country has implemented digital solutions in the form of mobile applications, web-based frameworks, and/or integrated platforms in which huge amounts of personal data are collected for various purposes (e.g., contact tracing, suspect search, and quarantine monitoring). These systems not only collect basic data about individuals but, in most cases, very sensitive data like their movements, spatio-temporal activities, travel history, visits to churches/clubs, purchases, and social interactions. While collection and utilization of person-specific data in different contexts is essential to limiting the spread of COVID-19, it increases the chances of privacy breaches and personal data misuse. Recently, many privacy protection techniques (PPTs) have been proposed based on the person-specific data included in different data types (e.g., tables, graphs, matrixes, barcodes, and geospatial data), and epidemic containment strategies (ECSs) (contact tracing, quarantine monitoring, symptom reports, etc.) in order to minimize privacy breaches and to permit only the intended uses of such personal data. In this paper, we present an extensive review of the PPTs that have been recently proposed to address the diverse privacy requirements/concerns stemming from the COVID-19 pandemic. We describe the heterogeneous types of data collected to control this pandemic, and the corresponding PPTs, as well as the paradigm shifts in personal data handling brought on by this pandemic. We systemically map the recently proposed PPTs into various ECSs and data lifecycle phases, and present an in-depth review of existing PPTs and evaluation metrics employed for analysis of their suitability. We describe various PPTs developed during the COVID-19 period that leverage emerging technologies, such as federated learning, blockchain, privacy by design, and swarm learning, to name a few. Furthermore, we discuss the challenges of preserving individual privacy during a pandemic, the role of privacy regulations/laws, and promising future research directions. With this article, our aim is to highlight the recent PPTs that have been specifically proposed for the COVID-19 arena, and point out research gaps for future developments in this regard.

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Privacy-preserving image search (PPIS): Secure classification and searching using convolutional neural network over large-scale encrypted medical images

Cited by 26 publications

References 12 publications

Privacy‐preserving CNN feature extraction and retrieval over medical images

Privacy‐preserving CNN feature extraction and retrieval over medical images

Compacting Deep Neural Networks for Internet of Things: Methods and Applications

A Comprehensive Analysis of Privacy Protection Techniques Developed for COVID-19 Pandemic

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