Privacy-preserving Feature Extraction Research Articles

Secureimagesec: A privacy-preserving framework for outsourced picture representation with content-based image retrieval

Content-Based Image Retrieval (CBIR) uses complicated algorithms to analyze visual attributes and retrieve relevant photos from large databases. CBIR is essential to a privacy-preserving feature extraction and protection method for outsourced picture representation. SecureImageSec combines essential methods with the system’s key entities to ensure secure, private and protected image feature processing during outsourcing. For a system to be implemented effectively, these techniques must be seamlessly integrated across critical entities, such as the client, the cloud server that is being outsourced, the component that protects secure features, the component that maintains privacy in communication, access control, and authorization, and the integration and system evaluation. The client entity initiates outsourcing using advanced encryption techniques to protect privacy. SecureImageSec protects outsourced data by using cutting-edge technologies like Fully Homomorphic Encryption (FHE) and Secure Multi-Party Computation (SMPC). Cloud servers hold secure feature protection entities and protect outsourced features’ privacy and security. SecureImageSec uses AES and FPE to protect data format. SecureImageSec’s cloud-outsourced privacy-preserving communication uses SSL/TLS and QKD to protect data transmission. Attribute-Based Encryption (ABE) and Functional Encryption (FE) in SecureImageSec limit access to outsourced features based on user attributes and allow fine-grained access control over decrypted data. SecureImageSec’s Information Leakage Rate (ILR) of 0.02 for a 1000-feature dataset shows its efficacy. SecureImageSec also achieves 4.5 bits of entropy, ensuring the encrypted feature set’s muscular cryptographic strength and randomness. Finally, SecureImageSec provides secure and private feature extraction and protection, including CBIR capabilities, for picture representation outsourcing.

Lightweight Privacy-Preserving Feature Extraction for EEG Signals Under Edge Computing

Lightweight Privacy-Preserving Feature Extraction for EEG Signals Under Edge Computing

Privacy‐preserving CNN feature extraction and retrieval over medical images

Online medicine diagnosis based on pathological images has been regarded as a pervasive method due to the advances in electronic healthcare and Internet of Things (IoT), however, it also causes storage and computing stress on the local IoT devices. To solve this problem, a nature way is to outsource images to cloud servers. Unfortunately, a range of security and privacy issues arise while delegating both storage and computing to the untrusted external servers. In this paper, we present a privacy-preserving feature extraction and retrieval scheme over medical images, which allows images storage and processing on two separate cloud servers. We share the images using secret sharing technology and design a set of secure two-party computation protocols between the two cloud servers. Then a privacy-preserving convolutional neural networks (CNN) framework is constructed to achieve feature extraction, classification and retrieval of images in the encrypted domain. We analyse and evaluate our scheme in terms of both security and efficiency. The results indicate that the proposed secure protocols in our scheme can significantly reduce the computation overhead while protecting the privacy of images as well as the data generated during execution on cloud servers and final results. The performance of our scheme in image feature extraction, classification and retrieval is at a similar level comparable to the scheme based on original CNN in plaintext.

Privacy-Preserving Color Image Feature Extraction by Quaternion Discrete Orthogonal Moments

To implement image storage and computation in cloud servers without violating users’ privacy, privacy-preserving feature extraction has been a new research interest. The existing works are mainly designed for grayscale images. For color images, they tend to convert them to grayscale images or obtain the results of the combination of single-channel processes. While the capabilities of features extracted from the encrypted color images will be affected if color information and inter-relationship between color channels are ignored. To fully preserve features of color images, we introduce quaternion theory to encode each color image and propose an improved vector homomorphic encryption scheme (IVHE) to encrypt quaternion-based color images. IVHE helps protect image content and keep vector characteristics of color images. Based on IVHE, the framework for feature extraction of privacy-preserving Quaternion Discrete Orthogonal Moments (PPQDOMs) is presented. Theoretical analyses prove that Quaternion Discrete Orthogonal Moments (QDOMs) can be extracted from the encrypted color images by PPQDOMs. Furthermore, we apply three common Discrete Orthogonal Moments to the proposed framework to evaluate its performance. Experimental results demonstrate that the proposed framework can protect color image content and perform well compared to QDOMs in image reconstruction and image recognition.

Secure Outsourcing Algorithm of BTC Feature Extraction in Cloud Computing

Advances in cloud computing have aroused many researchers’ interest in privacy-preserving feature extraction over outsourced multimedia data, especially private image data. Since block truncation coding (BTC) is known as a simple and efficient technology for image compression, this paper focuses on privacy-preserving feature extraction in BTC compressed domain. We propose a privacy-preserving computation of BTC feature extraction over massive encrypted images (also called PPBTC). First, all images are uploaded to the cloud after encryption. The privacy-preserving image encryption process consists of block permutation, pixel diffusion, and a bit-plane random shift. BTC features remain unchanged after encryption and the cloud server can directly extract BTC features from the encrypted images. Some analyses and experimental results demonstrate that the proposed privacy-preserving feature extraction scheme for BTC-compressed images is efficient and secure, and it can be applied to secure image computation applications in cloud computing.

Privacy-preserving Feature Extraction via Adversarial Training

Deep learning is increasingly popular, partly due to its widespread application potential, such as in civilian, government and military domains. Given the exacting computational requirements, cloud computing has been utilized to host user data and model. However, such an approach has potential privacy implications. Therefore, in this paper, we propose a method to protect user’s privacy in the inference phase of deep learning workflow. Specifically, we use an intermediate layer to separate the entire neural network into two parts, which are respectively deployed on the user device and the cloud server. The <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">encoder</i> , deployed on the user device, is used for raw data transformation, which removes the need for users to upload raw data to the cloud directly. However, we also demonstrate there exists potential for privacy leakage in the intermediate features of the neural network through two concrete experiments. In other words, the encoder on its own does not provide adequate privacy protection. Therefore, we also propose an approach to achieve <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Privacy-preserving Feature Extraction based on Adversarial Training (P-FEAT)</i> , where the goal of privacy attacking tasks and the goal of target tasks are adversarial in terms of sensitive attributes. By imposing privacy constraints during the feature extraction, we can reduce the contribution of the extracted features to the privacy leakage. In this way, privacy protection capability of the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">encoder</i> can be further strengthened. We then demonstrate the effectiveness of P-FEAT using a large number of experiments, whose findings show that P-FEAT can significantly reduce the threats of privacy attacking tasks while maintaining high accuracy of the target tasks.

PLCOM: Privacy-preserving outsourcing computation of Legendre circularly orthogonal moment over encrypted image data

In the image outsourcing system, image privacy is still an increasing concern since the cloud service provider and image owners are not in the same trusted domain. The most straightforward method for guaranteeing image privacy is to leverage cryptographic tools, but traditional cryptographic tools make feature extraction algorithms useless. To this end, we propose a privacy-preserving feature extraction scheme for Legendre circularly orthogonal moment, which is a novel global feature descriptor and can be used for image analysis. We first develop a novel feature descriptor, which is one of the circularly orthogonal moments and termed as Legendre Circularly Orthogonal Moment (LCOM). Then, we present a mathematical framework for implementing Privacy-preserving Legendre Circularly Orthogonal Moment (PLCOM) by combining LCOM and somewhat homomorphic encryption, and implement the image reconstruction in the encrypted domain based on PLCOM. Besides, the detailed theoretical analysis of message space and expanding factor generated by the quantitative technology shows that LCOM and image reconstruction in the plaintext domain can be realized with the aid of PLCOM. Finally, experimental results verify that the PLCOM’s performance in terms of image reconstruction capability and image recognition accuracy is acceptable.