A Privacy-Preserving Learning Framework for Face Recognition in Edge and Cloud Networks

Abstract

Offloading the computationally intensive workloads to the edge and cloud not only improves the quality of computation, but also creates an extra degree of diversity by collecting information from devices in service. Nevertheless, significant concerns on privacy are raised as the aggregated information could be misused without the permission by the third party. Sparse coding, which has been successful in computer vision, is finding application in this new domain. In this paper, we develop a secured face recognition framework to orchestrate sparse coding in edge and cloud networks. Specifically, 1). To protect the privacy, a low-complexity encrypting algorithm is developed based on random unitary transform, where its influence on dictionary learning and sparse representation is analysed. Furthermore, it is proved that such influence will not affect the accuracy of face recognition. 2). To fully utilize the multi-device diversity and avoid big data transmission between edge and cloud, a distributed learning framework is established, which extracts deeper features in an intermediate space, expanded according to the dictionaries from each device. Classification is performed in this new feature space to combat the noise and modeling error. Finally, the efficiency and effectiveness of the proposed framework is demonstrated through simulation results.

Design on Face Recognition System with Privacy Preservation Based on Homomorphic Encryption

“…The encryption time of this scheme is 15.7% higher than the encryption time in [18]. The total time of this scheme is 76.2% higher than the total time of literature [21], and the recognition accuracy of this scheme is 4.21%, 0.68%, 1.67% higher than that of literature [16], [17], and [19], respectively.…”

“…While ensuring the personal privacy and security of users, this scheme has better operational efficiency and practical value. Compared with the face recognition system in plaintext [7][8][9][10][11][12][13][14][15][16][17][18][19][20][21] proposed in recent years, this scheme has almost the same level on recognition accuracy and time efficiency.…”

Network Traffic Anomaly Detection: A Revisiting to Gaussian Process and Sparse Representation

“…In addition, if there exists more than one index where the associated traffic is anomaly, then any different combination of the positions of anomaly traffic corresponds to a distinguished class of Z j (t), which further pronounces the challenge of training data collection. To address this issue, we utilize SR [26], which has been successful in computer vision. The reason to adopt this technique is two-fold, Observing that most traffic anomalies affect the traffic volume in short term, i.e., only a small number of y(t − ( j + kU)V) is anomaly, we partition the formulated feature vectors into two categories as follows,…”

Privacy-Preserving Cloud Computing: Ecosystem, Life Cycle, Layered Architecture and Future Roadmap

“…Nevertheless, there is a growing concern about the privacy and security of data when it comes to machine learning. In [350], the authors have introduced a secure cloud-intelligent network framework that preserves the privacy of data as opposed to [351] that authors have produced a framwork that organizes sparse coding in edge and cloud networks. In this framework for recognizing noise and error of data, they have utilized classification.…”