LinkFlow: Efficient Large-Scale Inter-app Privacy Leakage Detection

AbstractWith the large-scale application of 5G in industrial production, the Internet of Things has become an important technology for various industries to achieve efficiency improvement and digital transformation with the help of the mobile edge computing. In the modern industry, the user often stores data collected by IoT devices in the cloud, but the data at the edge of the network involves a large of the sensitive information, which increases the risk of privacy leakage. In order to address these two challenges, we propose a security strategy in the edge computing. Our security strategy combines the Feistel architecture and short comparable encryption based on sliding window (SCESW). Compared to existing security strategies, our proposed security strategy guarantees its security while significantly reducing the computational overhead. And our GRC algorithm can be successfully deployed on a hardware platform.

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Image Perturbation-Based Deep Learning for Face Recognition Utilizing Discrete Cosine Transform

Electronics ◽

10.3390/electronics11010025 ◽

2021 ◽

Vol 11 (1) ◽

pp. 25

Author(s):

Jaehun Park ◽

Kwangsu Kim

Keyword(s):

Deep Learning ◽

Face Recognition ◽

Discrete Cosine Transform ◽

Large Scale ◽

User Study ◽

Emotion Classification ◽

Cosine Transform ◽

Privacy Leakage ◽

Attribute Classification ◽

Biometric Information

Face recognition, including emotion classification and face attribute classification, has seen tremendous progress during the last decade owing to the use of deep learning. Large-scale data collected from numerous users have been the driving force in this growth. However, face images containing the identities of the owner can potentially cause severe privacy leakage if linked to other sensitive biometric information. The novel discrete cosine transform (DCT) coefficient cutting method (DCC) proposed in this study combines DCT and pixelization to protect the privacy of the image. However, privacy is subjective, and it is not guaranteed that the transformed image will preserve privacy. To overcome this, a user study was conducted on whether DCC really preserves privacy. To this end, convolutional neural networks were trained for face recognition and face attribute classification tasks. Our survey and experiments demonstrate that a face recognition deep learning model can be trained with images that most people think preserve privacy at a manageable cost in classification accuracy.

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Personal Information Classification on Aggregated Android Application’s Permissions

Applied Sciences ◽

10.3390/app9193997 ◽

2019 ◽

Vol 9 (19) ◽

pp. 3997

Author(s):

Md Mehedi Hassan Onik ◽

Chul-Soo Kim ◽

Nam-Yong Lee ◽

Jinhong Yang

Keyword(s):

Large Scale ◽

Risk Model ◽

Personal Information ◽

Personal Data ◽

User Profile ◽

Application Programming Interface ◽

User Profiling ◽

Privacy Leakage ◽

Information Classification ◽

Google Play

Android is offering millions of apps on Google Play-store by the application publishers. However, those publishers do have a parent organization and share information with them. Through the ‘Android permission system’, a user permits an app to access sensitive personal data. Large-scale personal data integration can reveal user identity, enabling new insights and earn revenue for the organizations. Similarly, aggregation of Android app permissions by the app owning parent organizations can also cause privacy leakage by revealing the user profile. This work classifies risky personal data by proposing a threat model on the large-scale app permission aggregation by the app publishers and associated owners. A Google-play application programming interface (API) assisted web app is developed that visualizes all the permissions an app owner can collectively gather through multiple apps released via several publishers. The work empirically validates the performance of the risk model with two case studies. The top two Korean app owners, seven publishers, 108 apps and 720 sets of permissions are studied. With reasonable accuracy, the study finds the contact number, biometric ID, address, social graph, human behavior, email, location and unique ID as frequently exposed data. Finally, the work concludes that the real-time tracking of aggregated permissions can limit the odds of user profiling.

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