Privacy Protection Technology Research Articles

Privacy Protection Technology Based on Big Data Technology

Privacy Protection Technology Based on Big Data Technology

Research on Personal Medical Information Protection Based on Big Data

The rapid development of big data, cloud computing, and artificial intelligence has brought new opportunities to the development of the medical field. The emergence of electronic medical records and medical information systems has provided great convenience for people's medical treatment. However, the arrival of the big data era also poses new challenges to the protection of human medical privacy. The development and application of medical big data have led to explosive growth of data in the medical field, putting pressure on the management of medical institutions. The sharing of medical data between medical institutions and between medical institutions and third parties also poses significant security risks. Moreover, medical data leakage incidents have been continuously exposed in recent years, seriously infringing on the identity and privacy rights of patients. This article aims to explore the research on personal medical information protection based on big data. Firstly, the importance and necessity of protecting personal medical information were analyzed, as well as the current application status and existing problems of big data in medical information management. Secondly, a comparative analysis of domestic and international research on medical privacy protection was elaborated, including the analysis of medical information privacy protection standards in the United States, Europe, and Asia, and the differences and commonalities of international medical information privacy protection standards were compared and analyzed. Then, the theoretical basis of medical information privacy protection was summarized. Then, the application methods and technologies of big data in personal medical information protection were discussed. Finally, relevant countermeasures and suggestions for the protection of personal medical information in big data were discussed, including privacy protection technologies and policy recommendations, as well as ethical issues and norms for the protection of medical information privacy. Through this study, it is hoped that it can provide reference and inspiration for the protection of personal medical information in big data, and provide ideas and suggestions for future development directions.

Intelligent Internet of Things and Privacy Protection Technology for IPE Data Analysis

In the process of shaping the personality and forming the ideal and belief of innovative talents, education plays a unique role that cannot be replaced by other forms of education. The application of an intelligent Internet of Things has been well developed in college teaching. AI is the reality of technological development in the training of higher talents in the new era, and it is the realistic demand for IPE in the training of higher talents, which requires political and ideological empowerment. Positioning the role of "educational assistant" in AI and then defining it as a "quasi-subjective object", is the basic positioning of the introduction of higher-talent IPE at this stage. 1) Build a model of teacher-student interaction supported by "human-computer cooperation"; 2) Construct intelligent learning and intelligent teaching modes supported by "algorithm databases"; 3) Establish the mechanism principle of "people-oriented" assessment and goal orientation, and further improve the quality of IPE in China's higher education personnel training. This paper aims to explore the use of artificial intelligence technology to build an ideal ideological and political education paradigm in the process of training higher-level talents in Chinese universities.

Design and application of data security privacy protection technology in dance action sequence arrangement and display system

In view of the excessive consumption of difficult resources in traditional dance choreography, insufficient data in choreography and data leakage crisis in the system, this paper puts forward artificial intelligence technology to study the protection of dance choreography and system data security and privacy. In this paper, the directed graph neural network is used to intelligently arrange the dance movements, and the Hidden Markov Model (HMM) is used to process the dance scene data so that the output data is highly similar to the original data. In the protection of data security and privacy, the method of data encryption is used, and the encrypted data needs to be decrypted by algorithm for reading. The experimental results show that the more complete the dance movement display, the higher the dance recognition rate, and the recognition rate of the dynamic light projection algorithm is greater than 90 % regardless of several sets of dance movements, while the recognition rate of the traditional light projection algorithm is not up to the standard regardless of several sets of movements, and the recognition rate of the point cloud segmentation method is higher than 90 % only in 3–4 sets of dance and 5–6 sets of dance movements.

Construction of a network intelligence platform for privacy protection and integrated big data mining

MOTIVATION: With the enhancement of people’s awareness of the protection of personal privacy information, how to provide better services on the premise of protecting users’ privacy has become an urgent problem to be solved. Therefore, it is a necessary motivation to build a network intelligent platform for privacy protection and integrated big data mining. OBJECTIVE: In view of the existing network platform of data privacy leakage, low efficiency of data mining and user satisfaction is not high, this paper will adopt advanced privacy technology, to ensure the confidentiality of users’ personal information and security, to enhance the user trust and use experience, to better meet the needs of users. METHODS: In order to better protect the privacy of users, the network intelligent platform should adopt more advanced privacy protection technology. This paper uses the differential privacy algorithm to reduce the risk of data leakage and abuse, and ensure the accuracy and efficiency of data analysis and mining. In the design of the platform, the performance of the platform is fully taken into account to realize the secure storage and efficient processing of data, with good scalability and flexibility to meet the growing user needs and business needs. The performance of the network intelligent platform is also analyzed by experimental simulation. RESULT: The experimental results of this article indicated that in a network intelligent platform based on privacy protection and integrated big data mining, its data transmission encryption score was 9.5; the data storage encryption score was 9.8; the score of access control mechanism was 9.3; the privacy protection score was 9.6; the response time was 80 ms; the processing speed was 121GB/h; the user satisfaction rating was 6.6. CONCLUSION: This indicated that the network intelligent platform had good platform performance and user friendliness while ensuring data security and privacy protection. It could efficiently conduct data mining and ensure data security and privacy.

Privacy-Preserving Architectures for AI/ML Applications: Methods, Balances, and Illustrations

With the widespread integration of artificial intelligence (AI) and blockchain technologies, safeguarding privacy has become of paramount importance. These techniques not only ensure the confidentiality of individuals' data but also maintain the integrity and reliability of information. This study offers an introductory overview of AI and blockchain, highlighting their fusion and the subsequent emergence of privacy protection methodologies. It explores various application contexts, such as data encryption, de-identification, multi-tier distributed ledgers, and k-anonymity techniques. Moreover, the paper critically evaluates five essential dimensions of privacy protection systems within AI-blockchain integration: authorization management, access control, data security, network integrity, and scalability. Additionally, it conducts a comprehensive analysis of existing shortcomings, identifying their root causes and suggesting corresponding remedies. The study categorizes and synthesizes privacy protection methodologies based on AI-blockchain application contexts and technical frameworks. In conclusion, it outlines prospective avenues for the evolution of privacy protection technologies resulting from the integration of AI and blockchain, emphasizing the need to enhance efficiency and security for a more comprehensive safeguarding of privacy.

Privacy-Preserving Architectures for AI/ML Applications: Methods, Balances, and Illustrations

With the widespread integration of artificial intelligence (AI) and blockchain technologies, safeguarding privacy has become of paramount importance. These techniques not only ensure the confidentiality of individuals' data but also maintain the integrity and reliability of information. This study offers an introductory overview of AI and blockchain, highlighting their fusion and the subsequent emergence of privacy protection methodologies. It explores various application contexts, such as data encryption, de-identification, multi-tier distributed ledgers, and k-anonymity techniques. Moreover, the paper critically evaluates five essential dimensions of privacy protection systems within AI-blockchain integration: authorization management, access control, data security, network integrity, and scalability. Additionally, it conducts a comprehensive analysis of existing shortcomings, identifying their root causes and suggesting corresponding remedies. The study categorizes and synthesizes privacy protection methodologies based on AI-blockchain application contexts and technical frameworks. In conclusion, it outlines prospective avenues for the evolution of privacy protection technologies resulting from the integration of AI and blockchain, emphasizing the need to enhance efficiency and security for a more comprehensive safeguarding of privacy.

Challenges and Countermeasures of Federated Learning Data Poisoning Attack Situation Prediction

Federated learning is a distributed learning method used to solve data silos and privacy protection in machine learning, aiming to train global models together via multiple clients without sharing data. However, federated learning itself introduces certain security threats, which pose significant challenges in its practical applications. This article focuses on the common security risks of data poisoning during the training phase of federated learning clients. First, the definition of federated learning, attack types, data poisoning methods, privacy protection technology and data security situational awareness are summarized. Secondly, the system architecture fragility, communication efficiency shortcomings, computing resource consumption and situation prediction robustness of federated learning are analyzed, and related issues that affect the detection of data poisoning attacks are pointed out. Thirdly, a review is provided from the aspects of building a trusted federation, optimizing communication efficiency, improving computing power technology and personalized the federation. Finally, the research hotspots of the federated learning data poisoning attack situation prediction are prospected.

Dynamic feedback bit-level image privacy protection based on chaos and information hiding

Bit is the most basic unit of a digital image in the spatial domain, and bit-level encryption is regarded as an important technical means for digital image privacy protection. To address the vulnerability of image privacy protection to cryptographic attacks, in this paper, a bit-level image privacy protection scheme using Zigzag and chain-diffusion is proposed. The scheme uses a combination of Zigzag interleaving scrambling with chaotic sequences and chain-diffusion method images are encrypted at each bit level, while using non-sequential encryption to achieve efficient and secure encryption. To balance security and efficiency, the encryption strategy for each bit layer is weighted. The chaos-based sequences used for encryption depend on the previous hash value, thus the effect of chain-diffusion is achieved. To further enhance the encryption effect, a non-sequential encryption technique by non-linearly rearranging the bit cipher image is employed, so that the attacker cannot crack the protection scheme by analyzing the encrypted image. The ciphertext image hidden by discrete wavelet transform (DWT) also provides efficient encryption, higher level of security and robustness to attacks. This technology provides indistinguishable secret data embedding, making it difficult for attackers to detect or extract hidden information. Experimental results show that this scheme can effectively protect the confidentiality of the image and can resist various common cryptographic attacks. The scheme proposed in this paper is a preferred digital image privacy protection technology, so it has broad application prospects in image secure transmission occasions.

Develop and Disseminate Hands-on Lab Materials of Privacy Concepts and Technologies to Educators

In the era of digitalization, a massive amount of data has been generated from people’s online activities or use of portable/wearable devices. The data often carries rich information about people. Therefore, privacy technologies are needed, from data generation to usage and from transmission to storage, to protect people’s sensitive information. Although the research community is making great progress in addressing advanced privacy protection technologies, very few educational materials have been developed to incorporate the latest research results and engage students in learning privacy technologies, especially for younger generations. In this paper, we present our newly designed educational materials on privacy technologies, which can be used for training high quality cybersecurity professionals to meet the ever-increasing demand. The developed learning modules not only incorporate the latest research results in privacy technologies but also include effective hand-on lab activities. To help other institutions effectively teach privacy technologies, we organized a faculty training workshop in summer 2022. Twenty-nine faculty from twenty institutions nationwide participated in the training. Survey results show that the participants gained a better understanding of privacy issues and demonstrated strong interest in teaching privacy technologies after attending the workshop.

A Novel Federated Learning Framework Based on Conditional Generative Adversarial Networks for Privacy Preserving in 6G

With the rapid development of 6G networks, data transmission speed has significantly increased, making data privacy protection issues even more crucial. The federated learning (FL) is a distributed machine learning framework with privacy protection and secure encryption technology, aimed at enabling dispersed participants to collaborate on model training without disclosing private data to other participants. Nonetheless, recent research indicates that the exchange of shared gradients may lead to information disclosure, and thus FL still needs to address privacy concerns. Additionally, FL relies on a large number of diverse training data to forge efficient models, but in reality, the training data available to clients are limited, and data imbalance issues lead to over fitting in existing federated learning models. To alleviate these issues, we introduce a Novel Federated Learning Framework based on Conditional Generative Adversarial Networks (NFL-CGAN). NFL-CGAN divides the local networks of each client into private and public modules. The private module contains an extractor and a discriminator to protect privacy by retaining them locally. Conversely, the public module is shared with the server to aggregate the shared knowledge of clients, thereby improving the performance of each client local network. Comprehensive experimental analyses demonstrate that NFL-CGAN surpasses traditional FL baseline methods in data classification, showcasing its superior efficacy. Moreover, privacy assessments also verified robust and reliable privacy protection capabilities of NFL-CGAN.

Exploring the future application of UAVs: Face image privacy protection scheme based on chaos and DNA cryptography

In the application and promotion of UAV in the future, remote sensing image information is transmitted frequently, and its security issues will not be ignored. Aiming at the privacy security problem of portrait information in remote sensing images, this paper proposes a face privacy protection technology scheme based on chaos and DNA cryptography. Firstly, the edge recognition face detection technology is used to identify the face information, and the corresponding matrix is selected as the object of selective encryption. Then, the hash eigenvalues of the selected encrypted object are extracted, and the plaintext-associated chaotic sequences are generated for RGB permutation and zigzag interleaved scrambling in turn. Then, the three channels of RGB are encrypted by dynamic chain DNA encoding in turn. Finally, the cipher-image is obtained after performing discontinuous diffusion and lightweight bit-level confusion. In this paper, chaotic pseudo-random sequences with plaintext correlation and dynamic DNA chain encryption are used to effectively improve the ability of encryption system to resist cryptographic attacks. At the same time, selective encryption can effectively reduce the encryption complexity and performance overhead. Experimental results and security analysis show that the privacy protection scheme has excellent security and efficiency performance. Therefore, the face privacy protection technology scheme reported in this paper has excellent performance and has broad application prospects in UAV and remote sensing communication.

Retracted: Privacy Protection Technology Based on Machine Learning and Intelligent Data Recognition

Retracted: Privacy Protection Technology Based on Machine Learning and Intelligent Data Recognition

Research on Location Privacy Protection Technology in Wireless Sensor Networks Based on Big Data

The digital footprint in wireless sensor networks can bring great academic and commercial value, but it will also bring the risk of privacy disclosure to users. This study discusses the location privacy protection methods in location based service under multiple scenarios. In the experiment, a false location filtering algorithm for real-time location request scenarios and a false path generation model for offline location release scenarios are proposed. The false position filtering algorithm is implemented based on the idea of a large top heap selection query. The algorithm can construct false position candidate sets and filter false positions. The false track generation model combines the false position technology and the generative adversarial networks model, which mainly protects the user’s track data by synthesizing tracks. In the attacker’s recognition experiment of a real location. The minimum distance between the false locations generated by the algorithm proposed in the study is above 400 m and the generation time does not exceed 5 ms, generating a better set of false locations in terms of both effectiveness and efficiency. Compared with several commonly used privacy-preserving algorithms, the proposed algorithm has the lowest probability of being identified with real locations, with no more than 21% overall, and is almost independent of the k value. the recognition accuracy of the trajectory user link task decreases from over 90% to about 34%, indicating that the proposed fake trajectory generation model can effectively protect users’ data privacy. The experimental results demonstrate that the algorithm and model proposed in the study can quickly generate physically dispersed and semantically diverse sets of fake locations and effectively protect users’ trajectory privacy, which is important for users’ digital footprint privacy protection.

Privacy-Preserving AI/ML Application Architectures: Techniques, Trade-offs, and Case Studies

Given the widespread adoption and fusion of artificial intelligence (AI) and blockchain technologies, safeguarding privacy has become paramount. These techniques not only ensure the confidentiality of individuals' data but also uphold the integrity and reliability of the information. This study provides an introductory overview of AI and blockchain, elucidating their fusion and subsequent emergence of privacy protection methodologies. It delves into specific application contexts such as data encryption, de-identification, multi-tier distributed ledgers, and k-anonymity techniques. Furthermore, the paper critically assesses five pivotal dimensions of privacy protection systems within AI-blockchain integration: authorization management, access control, data security, network integrity, and scalability. Additionally, it conducts a thorough analysis of existing shortcomings, pinpointing their root causes and proposing corresponding remedies. The study also categorizes and synthesizes privacy protection methodologies based on AI-blockchain application contexts and technical frameworks. In conclusion, it outlines prospective avenues for the evolution of privacy protection technologies stemming from the integration of AI and blockchain, emphasizing the need to enhance efficiency and security for a more holistic safeguarding of privacy.

An Automatic Privacy-Aware Framework for Text Data in Online Social Network Based on a Multi-Deep Learning Model

With the increasing severity of user privacy leaks in online social networks (OSNs), existing privacy protection technologies have difficulty meeting the diverse privacy protection needs of users. Therefore, privacy-aware (PA) for the text data that users post on OSNs has become a current research focus. However, most existing PA algorithms for OSN users only provide the types of privacy disclosures rather than the specific locations of disclosures. Furthermore, although named entity recognition (NER) technology can extract specific locations of privacy text, it has poor recognition performance for nested and interest privacy. To address these issues, this paper proposes a PA framework based on the extraction of OSN privacy information content. The framework can automatically perceive the privacy information shared by users in OSNs and accurately locate which parts of the text are leaking sensitive information. Firstly, we combine the roformerBERT model, BI_LSTM model, and global_pointer algorithm to construct a direct privacy entity recognition (DPER) model for solving the specific privacy location recognition and entity nesting problems. Secondly, we use the roformerBERT model and UniLM framework to construct an interest privacy inference (IPI) model for interest recognition and to generate interpretable text that supports this interest. Finally, we constructed a dataset of 13,000 privacy-containing texts for experimentation. Experimental results show that the overall accuracy of the DPER model can reach 91.80%, while that of the IPI model can reach 98.3%. Simultaneously, we compare the proposed model with recent methods. The analysis of the results indicates that the proposed model exhibits better performance than previous methods.

A solution to smart health and state of art

A medical cyber–physical system (MCPS) is a unique cyber–physical system (CPS), which combines embedded software control devices, networking capabilities, and complex physiological dynamics of patients in the modern medical field. In the process of communication, device, and information system interaction of MCPS, medical cyber–physical data are generated digitally, stored electronically, and accessed remotely by medical staff or patients. With the advent of the era of medical big data, a large amount of medical cyber–physical data is collected, and its sharing provides great value for diagnosis, pathological analysis, epidemic tracking, pharmaceutical, insurance, and so on. This overview will present MCPS’s architectures and frameworks from different perspectives, modeling and verification methods, identification and sign sensing technologies, key communications’ technologies, data storage and analysis technologies, monitoring systems, data security and privacy protection technologies, and key research perspectives and directions. We can have a com- prehensive understanding of the important characteristics and technical route of MCPS, and grasp its research status and progress.

Privacy and integrity-preserving data aggregation scheme for wireless sensor networks digital twins

The security technology of digital twin is an important guarantee to ensure the security of digital twin operation, which mainly includes network security technology, data security technology and privacy protection technology. In wireless sensor networks, data aggregation technologies are known as a suitable solution to reduce energy consumption. In addition, due to wireless communications, wireless sensor networks are subject to many attacks. Therefore, it is very important to provide data security in the data aggregation process. In this paper, in order to protect data privacy and verify data integrity, moreover, balance the energy consumption and security during the data aggregation, we present a privacy and integrity–preserving data aggregation scheme for wireless sensor networks based on digital twins technology and homomorphic fingerprinting (HFPIDA). The HFPIDA adopts privacy function to protect data privacy and adopts homomorphic fingerprinting technology to verify the aggregation data integrity. Security analysis shows that the HFPIDA can effectively preserve data privacy and verify data integrity. Simulation results show that the HFPIDA requires less communication and energy overheads, and can achieve higher aggregation accuracy.

A review of secure federated learning: Privacy leakage threats, protection technologies, challenges and future directions

Advances in the new generation of Internet of Things (IoT) technology are propelling the growth of intelligent industrial applications worldwide. Simultaneously, widespread adoption of artificial intelligence (AI) technologies, such as machine and deep learning, is accelerating. Traditional machine learning models rely heavily on massive amounts of data, however collecting and processing massive amounts of data generated by network-edge devices is costly and inefficient, and poses serious risks to data privacy. As a new paradigm for statistical model training in distributed edge networks, federated learning (FL) enables data to participate in federated model training without being localized. This approach can be used to solve traditional machine learning problems of low data utilization, data privacy, and information security caused by data isolation. However, the defects of the FL framework and insecure network environments cause many security and privacy leakage problems in actual application scenarios of FL. First, the concepts, classifications, and fundamental FL principles were described. Second, the mainstream privacy security issues and classification of FL were investigated. Privacy security protection techniques for FL were then identified. Finally, challenges and future research directions for the development of FL privacy security are discussed.

A Triple Real-Time Trajectory Privacy Protection Mechanism Based on Edge Computing and Blockchain in Mobile Crowdsourcing

With the rapid development of the Internet of Things (IoT) and the rapid popularization of 5G networks, the data that needs to be processed in Mobile Crowdsourcing (MCS) system is increasing every day. Traditional cloud computing can no longer meet the needs of crowdsourcing for real-time data and processing efficiency, thus, edge computing was born. Edge computing can be calculated at the edge of network so that greatly improve the efficiency and real-time performance of data processing. In addition, most of the existing privacy protection technologies are based on the trusted third parties. Therefore, in view of the semi-trustworthiness of edge servers and the transparency of blockchain, this paper proposes a triple real-time trajectory privacy protection mechanism (T-LGEB) based on edge computing and blockchain. Through combining the localized differential privacy and multiple probability extension mechanism, the T-LGEB mechanism is proposed to send the requests and data to the edge server in this paper. Then, through the spatio-temporal dynamic pseudonym mechanism proposed in the paper, the entire trajectory of task participants is divided into multiple unrelated trajectory segments with different pseudonymous identities in order to protect the trajectory privacy of task participants while ensuring high data availability and real-time data. Through a large number of experiments and comparative analysis on multiple real data sets, the proposed T-LGEB has extremely high privacy protection capabilities and data availability, and the resource consumption caused is relatively low.