Privacy Protection Method Research Articles

Cloud computing data privacy protection method based on blockchain

Cloud computing data privacy protection method based on blockchain

Cloud computing data privacy protection method based on blockchain

Cloud computing data privacy protection method based on blockchain

A Privacy Protection Framework for Medical Image Security without Key Dependency Based on Visual Cryptography and Trusted Computing

The development of mobile Internet and the popularization of intelligent sensor devices greatly facilitate the generation and transmission of massive multimedia data including medical images and pathological models on the open network. The popularity of artificial intelligence (AI) technologies has greatly improved the efficiency of medical image recognition and diagnosis. However, it also poses new challenges to the security and privacy of medical data. The leakage of medical images related to users’ privacy is emerging one after another. The existing privacy protection methods based on cryptography or watermarking often bring a burden to image transmission. In this paper, we propose a privacy‐preserving recognition network for medical images (called MPVCNet) to solve these problems. MPVCNet uses visual cryptography (VC) to transmit images by sharing. Benefiting from the secret‐sharing characteristics of VC, MPVCNet can securely transmit images in clear text, which can both protect privacy and mitigate performance loss. Aiming at the problem that VC is easy to forge, we combine trusted computing environments (TEE) and blind watermarking technologies to embed verification information into sharing images. We further leverage the transfer learning technology to abate the side effect resulting from the use of visual cryptography. The results of the experiment show that our approach can maintain the trustworthiness and recognition performance of the recognition networks while protecting the privacy of medical images.

An Effective Differential Privacy Protection Method of Location data Based on Perturbation Loss Constraint

An Effective Differential Privacy Protection Method of Location data Based on Perturbation Loss Constraint

A privacy protection method for IoT nodes based on convolutional neural network

A privacy protection method for IoT nodes based on convolutional neural network

PrivacyMask: Real-world privacy protection in face ID systems

Recent works have illustrated that many facial privacy protection methods are effective in specific face recognition algorithms. However, the COVID-19 pandemic has promoted the rapid innovation of face recognition algorithms for face occlusion, especially for the face wearing a mask. It is tricky to avoid being tracked by artificial intelligence only through ordinary props because many facial feature extractors can determine the ID only through a tiny local feature. Therefore, the ubiquitous high-precision camera makes privacy protection worrying. In this paper, we establish an attack method directed against liveness detection. A mask printed with a textured pattern is proposed, which can resist the face extractor optimized for face occlusion. We focus on studying the attack efficiency in adversarial patches mapping from two-dimensional to three-dimensional space. Specifically, we investigate a projection network for the mask structure. It can convert the patches to fit perfectly on the mask. Even if it is deformed, rotated and the lighting changes, it will reduce the recognition ability of the face extractor. The experimental results show that the proposed method can integrate multiple types of face recognition algorithms without significantly reducing the training performance. If we combine it with the static protection method, people can prevent face data from being collected.

How Can AI Help Improve Food Safety?

With advances in artificial intelligence (AI) technologies, the development and implementation of digital food systems are becoming increasingly possible. There is tremendous interest in using different AI applications, such as machine learning models, natural language processing, and computer vision to improve food safety. Possible AI applications are broad and include, but are not limited to, (a) food safety risk prediction and monitoring as well as food safety optimization throughout the supply chain, (b) improved public health systems (e.g., by providing early warning of outbreaks and source attribution), and (c) detection, identification, and characterization of foodborne pathogens. However, AI technologies in food safety lag behind in commercial development because of obstacles such as limited data sharing and limited collaborative research and development efforts. Future actions should be directed toward applying data privacy protection methods, improving data standardization, and developing a collaborative ecosystem to drive innovations in AI applications to food safety.

Privacy protection methods of location services in big data

Abstract The rapid development of mobile communication technology not only brings convenience and fun to our life, but also brings a series of problems such as privacy disclosure. Therefore, it is very necessary to study the privacy protection method based on location service to strengthen the security of location privacy. The purpose of this work is to improve the security of location privacy and prevent the disclosure of user privacy by studying the characteristics of location services and privacy protection methods. This article first describes the characteristics of the important location privacy protection law, and then studies the structural characteristics and operation process of the location privacy protection law. This work evaluates the advantages and disadvantages of different methods, and finally compares the performance of several privacy protection algorithms through experimental analysis. Through the research of hiding space method, two-level cache method based on user grid, differential privacy protection method and experimental analysis of the algorithm, an effective privacy protection algorithm can be obtained. It can better protect the location privacy of users. For example, dual-active in the hidden space algorithm has the best privacy protection performance. Compared with other algorithms, the success rate of generating hidden space is increased by more than 10%, and the time of generating hidden space is shortened by about a quarter. The algorithm It has certain practical value and significance for use in the privacy protection of users.

Privacy-Preserving Classification Scheme Based on Support Vector Machine

As a classifier, support vector machine (SVM) explains a core problem of machine learning, namely sample classification in statistical terms. It has been widely used in machine learning, data mining, pattern recognition, and other fields. With the wide applications of SVMs in machine learning and big data, privacy protection of sensitive data in SVMs is becoming more and more important, such as face recognition and biometric information. At present, the main privacy protection methods in SVMs are homomorphic encryption and secure multiparty computation. However, there are some problems in current research. The computational efficiency is low, and the scalability of the schemes is poor. In addition, the user must stay online in some solutions. To solve the abovementioned problems, this article designs a secure and efficient classification scheme based on SVM to protect the privacy of private data and support vectors in the calculation and transmission process. First, the distributed two trapdoors public-key cryptosystem proposed by Liu is used to realize the distributed double-key decryption function, weaken the decryption capability of a cloud server with the master key, and prevent the server from launching active attacks. Second, we design a universal secure computing protocol for nonlinear SVMs based on the Gaussian kernel function, which can be extended to polynomial kernel function, sigmoid kernel function and is suitable to different kernel functions. Compared with existing schemes, our solution reduces the amount of encrypted data, simplifies the calculation process, and improves calculation efficiency. Third, an introduced cloud server realizes user offline function. Finally, we analyze the security of the scheme and verify its efficiency through experiments. Analysis and experimental results show that the scheme has the advantages of high efficiency, good scalability, and user offline function.

Online Dating Scam Victims Psychological Impact Analysis

In an era of widespread mass marketing scams on the Internet, many victims have reported varying degrees of financial loss and psychological damage after encountering lottery scams and advance payment scams. Among them, the emotional damage to victims of online dating scams may be even more severe because the whole scam process involves mental attachment, sexual abuse, and relationship breakdown. There is little help and support for victims throughout the scam process and even after the scam is over, which not only makes it difficult for victims to get timely and professional assistance after experiencing online scams, but victims even run the risk of being scammed by criminals again afterward, so timely help and professional psychological treatment for victims is of positive significance. In previous studies, there are fewer reports summarizing and analyzing the psychological conditions of victims. In this paper, we will summarize the psychological problems of victims of online dating scams and compare the psychological treatment needed by victims of online dating scams who experience online aggression with the psychological treatment needed by victims who are actually violated in reality. For example, professional treatment for real victims of sexual assault has methodological merit for victims of online fraud, methods of privacy and psychological protection for victims of online romance fraud, criteria for measuring psychological status, and more. These are all measures to promote enhanced victim care and the prevention of online dating fraud throughout the industry.

Differential privacy protection method for trip‐oriented shared data

SummaryWhile location information sharing technology provides convenience for unmanned driving and journey navigation, user journey information sharing has also become a disaster for privacy information leakage. The traditional differential privacy method can only perturb the data entirely and cannot consider the design of data availability. In this paper, the difference privacy algorithm is improved by combining it with the Apriori algorithm, and the relevant perturbation is carried out after mining the associated data of the user's trip. In the face of possible data attacks, the privacy protection of the sensitive information of the user's actual data is ensured while the availability of the data is ensured. By testing 3000 trip data generated by experimental simulation, the results show that the correlation information between the original datasets is destroyed. However good availability is maintained after the Laplace data perturbation of the proposed algorithm for both simultaneous and multi‐person trips.

Smart Home Privacy Protection Methods against a Passive Wireless Snooping Side-Channel Attack.

Smart home technologies have attracted more users in recent years due to significant advancements in their underlying enabler components, such as sensors, actuators, and processors, which are spreading in various domains and have become more affordable. However, these IoT-based solutions are prone to data leakage; this privacy issue has motivated researchers to seek a secure solution to overcome this challenge. In this regard, wireless signal eavesdropping is one of the most severe threats that enables attackers to obtain residents' sensitive information. Even if the system encrypts all communications, some cyber attacks can still steal information by interpreting the contextual data related to the transmitted signals. For example, a "fingerprint and timing-based snooping (FATS)" attack is a side-channel attack (SCA) developed to infer in-home activities passively from a remote location near the targeted house. An SCA is a sort of cyber attack that extracts valuable information from smart systems without accessing the content of data packets. This paper reviews the SCAs associated with cyber-physical systems, focusing on the proposed solutions to protect the privacy of smart homes against FATS attacks in detail. Moreover, this work clarifies shortcomings and future opportunities by analyzing the existing gaps in the reviewed methods.

Research on Information Leakage Tracking Algorithms in Online Social Networks

An online social network is a platform where people can communicate with friends, share information, speed up business development, and improve teamwork. A large amount of user privacy information existing in real social networks is leaked from person to person, and this issue has hardly been studied. With the rapid expansion of the network, the issue of privacy protection has received increasing attention. So far, many privacy protection methods including differential protection algorithms, encryption algorithms, access control strategies, and anonymization have been researched and applied. Information leakage means that the information shared by the user is disseminated or downloaded by his friends without the user's consent, and the transmission of private information will not be recorded. In order to track and find out the ways and methods of information leakage, this article adopts an unusual method, namely, the probability judgment based on trust. By screening the similarities between users, past information exchanges, and the topology of social networks, a trust model is established to evaluate and estimate the degree of trust between users. According to the rating information privacy of friends' trust, an information dissemination system is established, which can be applied to online social networking platforms to reduce the risk of information leakage, thereby ensuring the security of users' private information. At the same time, this paper expands the transmission system model without user authorization and proposes a fingerprint-based deterministic leak tracking algorithm.

An improved federated learning approach enhanced internet of health things framework for private decentralized distributed data

With the privacy protection increasingly being concerned, Data centralization often heavily causes a big risk of privacy protection, gradually, there is a prevailing trend to enhance the security performance by means of data decentralization, above all, for health care internet of things (IoT) data. Meanwhile, Federated learning has obvious privacy advantages compared to data center training on protecting privacy data. For this reason, a novel framework based on federated learning is presented in this paper, which is suitable for private and decentralized data sets, such as big data in healthy Internet of Things. Specifically, the main work of the puts forward framework includes: (1) Multi-center data collection of healthy Internet of Things. (2) healthy data analysis of Internet of Things. (3) privacy protection method for data of healthy Internet of Things. Finally, related experiments show that the proposed method is feasible, and compared with the traditional methods, it has significantly improved the performance in Quality of Service (QoS) and IoUs indicator.

Novel trajectory privacy protection method against prediction attacks

When the public uses Location-based Services (LBSs), their location information is constantly exposed. Owing to the spatiotemporal correlation of trajectories, it is easy for attackers to use historical trajectories and background knowledge to predict future locations of target users. We refer to this type of inference attack as a trajectory prediction attack. To address such potential but threatening attacks in a continuous location query, we propose a novel trajectory privacy protection method. The proposed algorithm aims to generate an indistinguishable perturbed location that is robust to the prediction attack, wherein the user’s real location can be replaced by a perturbed location when submitted to an untrusted server. First, a hidden Markov model-based trajectory prediction mechanism is proposed to simulate predictive attacks and compute the predictability of positions before the trajectory is released. Second, the w sliding window mechanism is designed to dynamically adjust the privacy protection degree of each location point in the trajectory according to the predictability of the location and privacy needs of users. Finally, we propose a bounded noise-adding algorithm based on the Laplace mechanism to improve the usability of data. In our experiments, mutual information, trajectory root-mean-square error, query error, and root-mean-square predictability were used as evaluation criteria, and the performance of the proposed method was comprehensively evaluated. The results show that our algorithm can reduce the trajectory predictability to 0.21 without reducing data availability, which is effective against prediction attacks.

Trusted Computing and Privacy Protection Method for Computer IoT Nodes Based on Fuzzy Logic Blockchain

With the continuous growth of the social economy, the trusted computing and privacy protection methods of IoT nodes are constantly innovating and changing, from the traditional single IoT node protection method to the new interactive IoT nodes supplemented by various network resources. That is, the item information server encrypts the detailed information of the item layer by layer with the session key of the adjacent node according to the node sequence of the response path. By completing the complete interactive design and the protection knot in the unique illusory environment, the application and discussion of privacy protection is achieved in the context of Internet of Things, creating a design concept for trusted computing and privacy protection methods for IoT nodes, and discussing the rationality of interactive design in the process of trusted computing and privacy protection methods for IoT nodes. The simulation results show that the application of fuzzy logic blockchain is very extensive, and the trusted computing and privacy protection methods of IoT nodes have a broader platform and a more open environment.

Fairness and privacy preservation for facial images: GAN-based methods

Facial images are widely adopted for computer vision tasks such as face recognition or attribute classifications. Consequently, the adoption of mass real facial images leads to significant identification privacy leakage concerns. Meanwhile, the model classification results suffer unfair predictions towards features such as genders due to biased training data distributions. Although methods have been proposed to resolve the privacy and fairness issues separately, simultaneous protection methods are merely studied. In this study, for facial attributes classifications, we propose one unified framework with GAN models to generate synthetic images for privacy protections and contrastive learning based loss designs to enforce fairness protections simultaneously. Meanwhile, unlike other privacy or fairness protection methods, the proposed methods can maintain high data and model utilities. We evaluate our approaches with the high image resolution dataset CelebA-HD, and the results show our methods meet both privacy and fairness requirements.

Blockchain data privacy protection modeling based on CP-ABE algorithm

Abstract With the proposal of the concept of ubiquitous power Internet of Things and the further promotion of blockchain strategy, modern information technologies such as the Internet of Things and cloud computing have been widely used in the power system. While the power grid is becoming more intelligent, it is also accompanied by the emergence of various security threats, and the power grid will face greater challenges and pressures. In this paper, aiming at the current situation of information security faced by the power grid blockchain under the cloud Internet of Things, a multi-authorization center ciphertext policy attribute encryption access control method is constructed for the power grid blockchain. It adds the function of visitor attribute update revocation, aggregates multi-dimensional data into ciphertext, and uses CP-ABE encryption to implement fine-grained access control at the dimension level. Through experimental analysis, in terms of security and functionality, this method can resist physical collision attacks and manage access rights flexibly. In terms of encryption and decryption efficiency, the encryption and decryption calculation speed of the proposed method is 6.2–15.6% higher than that of the traditional method, and the time consumption efficiency is more than 5% higher than that of other methods. It proves that this model has more advantages for open network environments such as power grid blockchain with high confidentiality requirements and large scale of users. It is of great significance to study the data security and privacy protection methods of the power grid blockchain.

Privacy Preserving Technologies in US Education.

In the US education sector, data are captured on learners at all stages of the life course with rich, sensitive information on learner demographics, enrollment, achievement, borrowing, and outcomes. Most data are controlled by institutions, who increasingly want to monitor progress from cradle to career, tracking students from early childhood through primary and secondary schools (K-12), on to degrees granted in postsecondary institutions, including continuing and technical education that grant credentials. Collecting, storing, and using such data raises privacy concerns.
 This presentation describes privacy preserving technologies (PPTs) that have been discussed, tested, implemented – and abandoned – in the education domain. These include both input and output privacy protection methods ranging from absolute lockdowns of learner data to cryptographically-protected data access and analysis systems. We describe most familiar and tested PPTs including secure hashing, secure multiparty computation, trusted execution environments, and differential privacy, highlighting actual applications in schools, departments of education, and educational technology nonprofits and companies.
 We summarize the factors that limit PPT use and success, including legal, institutional, technical, and cultural barriers and offer recommendations to overcome these barriers. For example, we find gaps in understanding what PPTs are and what problems they solve; a lack of engagement with data controllers, subjects, and regulators; and a lack of human capital to implement and use PPTs. There are also significant research and development needs to continue making PPTs less costly in financial, compute, and complexity terms. Our recommendations suggest topics for continued research into methods that produce disaggregate statistics to analyze equity while protecting privacy, have clear and fair privacy loss budgets, and produce open-source technology, ideally pairing scientists with education experts.

Remote Surveillance System without Privacy Leakage for Contacts in Infectious Disease

Remote surveillance is an effective method in restraining the spread of infectious diseases via monitoring crowds in affected areas. However, the monitoring targets in existing works are crowds, leading to high system cost, and most of them focus on finding close contacts, less considering the privacy protection and suspected treatment issues. To conquer above problems, this paper develops a new remote surveillance system for infectious diseases, which contains the following major contributions: (1) the monitoring targets are the ordinary contacts not all crowds, effectively decreasing the system cost; (2) establish the joint-control mechanism among contacts, health center, and the hospital to facilitate the prediagnosis and in-time treatment for suspected patients; (3) to avoid the privacy leakage of contacts, a double encryption strategy is designed to protect the location information, and a separating database-storage mechanism is used to improve the contact’s data security on the whole. Theoretical security analysis showed that the proposed system and privacy protection methods can effectively fight transmission attacks and avoid privacy leakage during data usage. Based on the created COVID-19 dataset, the simulation experiments were carried out to evaluate the effectiveness and feasibility of the proposed system, on the metrics of the accuracy of prediagnosis, encryption, and decryption time for health and location data. In summary, this work provides a promising way of low-cost remote surveillance system without privacy leakage to control the spreading of infectious disease.