Privacy-preserving content-based image retrieval for mobile computing

Wang, Yong; Liu, Fei; Pang, Zimao; Hassan, Abdelrhman

doi:10.1016/j.jisa.2019.102399

Cited by 14 publications

(9 citation statements)

References 18 publications

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“…In [34], the authors introduced a method that supports cloud server-based local feature extraction such as SIFT, image similarity scoring, and Index building. They proposed a multi-index for SIFT descriptors, the encrypted image was loaded, and the model returned similar images from the database in response to a query image.…”

Section: Related Workmentioning

confidence: 99%

A Novel Hybrid Approach for a Content-Based Image Retrieval Using Feature Fusion

2023

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The multimedia content generated by devices and image processing techniques requires high computation costs to retrieve images similar to the user’s query from the database. An annotation-based traditional system of image retrieval is not coherent because pixel-wise matching of images brings significant variations in terms of pattern, storage, and angle. The Content-Based Image Retrieval (CBIR) method is more commonly used in these cases. CBIR efficiently quantifies the likeness between the database images and the query image. CBIR collects images identical to the query image from a huge database and extracts more useful features from the image provided as a query image. Then, it relates and matches these features with the database images’ features and retakes them with similar features. In this study, we introduce a novel hybrid deep learning and machine learning-based CBIR system that uses a transfer learning technique and is implemented using two pre-trained deep learning models, ResNet50 and VGG16, and one machine learning model, KNN. We use the transfer learning technique to obtain the features from the images by using these two deep learning (DL) models. The image similarity is calculated using the machine learning (ML) model KNN and Euclidean distance. We build a web interface to show the result of similar images, and the Precision is used as the performance measure of the model that achieved 100%. Our proposed system outperforms other CBIR systems and can be used in many applications that need CBIR, such as digital libraries, historical research, fingerprint identification, and crime prevention.

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Section: Related Workmentioning

confidence: 99%

A Novel Hybrid Approach for a Content-Based Image Retrieval Using Feature Fusion

2023

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“…Wang et al (2019b) examining the challenging computing discharge problem in multi-access edge computing (MEC) systems. We implement the MEC program and describe the problem of offloading.…”

Section: Related Workmentioning

confidence: 99%

“…Wang et al (2019a) proposing a privacy-preserving content-based image retrieval (CBIR) system for mobile cloud computing that embraces scale-invariant feature transform (SIFT) features for similar image retrieval. Data such as extraction of SIFT features, generation of image index and retrieval of images are moved to cloud servers.…”

Section: Related Workmentioning

confidence: 99%

Emphasizing privacy and security of edge intelligence with machine learning for healthcare

Rajendran

Mathivanan

Jayagopal

et al. 2021

IJICC

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PurposeArtificial Intelligence (AI) has surpassed expectations in opening up different possibilities for machines from different walks of life. Cloud service providers are pushing. Edge computing reduces latency, improving availability and saving bandwidth.Design/methodology/approachThe exponential growth in tensor processing unit (TPU) and graphics processing unit (GPU) combined with different types of sensors has enabled the pairing of medical technology with deep learning in providing the best patient care. A significant role of pushing and pulling data from the cloud, big data comes into play as velocity, veracity and volume of data with IoT assisting doctors in predicting the abnormalities and providing customized treatment based on the patient electronic health record (EHR).FindingsThe primary focus of edge computing is decentralizing and bringing intelligent IoT devices to provide real-time computing at the point of presence (PoP). The impact of the PoP in healthcare gains importance as wearable devices and mobile apps are entrusted with real-time monitoring and diagnosis of patients. The impact edge computing of the PoP in healthcare gains significance as wearable devices and mobile apps are entrusted with real-time monitoring and diagnosis of patients.Originality/valueThe utility value of sensors data improves through the Laplacian mechanism of preserved PII response to each query from the ODL. The scalability is at 50% with respect to the sensitivity and preservation of the PII values in the local ODL.

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“…Despite their benefits, MCC apps raise concerns as users provide a wide range of personal data to them (e.g. Evernote, Dropbox, OneDrive) (Noor et al , 2018; Wang et al , 2019). Prior research recognizes mobile app users’ concerns and categorizes them into privacy concerns and security concerns (Balapour et al , 2020).…”

Section: Introductionmentioning

confidence: 99%

Post hoc security and privacy concerns in mobile apps: the moderating roles of mobile apps’ features and providers

Nikkhah

Grover

Sabherwal

2023

ICS

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Purpose This study aims to argue that user’s continued use behavior is contingent upon two perceptions (i.e. the app and the provider). This study examines the moderating effects of user’s perceptions of apps and providers on the effects of security and privacy concerns and investigate whether assurance mechanisms decrease such concerns. Design/methodology/approach This study conducts a scenario-based survey with 694 mobile cloud computing (MCC) app users to understand their perceptions and behaviors. Findings This study finds that while perceived value of data transfer to the cloud moderates the effects of security and privacy concerns on continued use behavior, trust only moderates the effect of privacy concerns. This study also finds that perceived effectiveness of security and privacy intervention impacts privacy concerns but does not decrease security concerns. Originality/value Prior mobile app studies mainly focused on mobile apps and did not investigate the perceptions of app providers along with app features in the same study. Furthermore, International Organization for Standardization 27018 certification and privacy policy notification are the interventions that exhibit data assurance mechanisms. However, it is unknown whether these interventions are able to decrease users’ security and privacy concerns after using MCC apps.

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Privacy-preserving content-based image retrieval for mobile computing

Cited by 14 publications

References 18 publications

A Novel Hybrid Approach for a Content-Based Image Retrieval Using Feature Fusion

A Novel Hybrid Approach for a Content-Based Image Retrieval Using Feature Fusion

Emphasizing privacy and security of edge intelligence with machine learning for healthcare

Post hoc security and privacy concerns in mobile apps: the moderating roles of mobile apps’ features and providers

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