Big Data Privacy Protection Research Articles

Privacy Protection in Mobile Big Data: Challenges and Solutions

The pervasive use of mobile big data has profoundly altered daily life, providing unprecedented convenience and efficiency. However, with the proliferation of mobile devices and the explosive growth of data volume, the issue of user privacy protection has become increasingly severe. Location information and semantic information, as the two core components of mobile big data, can directly reflect users’ behavioral trajectories and thought dynamics, underscoring the importance of privacy protection. Although existing technologies can protect user data to a certain extent, traditional methods struggle to address increasingly sophisticated attack techniques in the face of evolving privacy threats. A comprehensive privacy protection scheme for mobile big data was proposed in this study, with a focus on two main areas: the privacy protection of location-based and semantic-based mobile big data. For location information protection, an uncertain graph model was employed to effectively resist combined attacks by jointly protecting the user layer and the location layer. For semantic information protection, a hypergraph clustering method was used to structurally protect the user layer and the semantic layer, enhancing the privacy security of semantic information. This study not only addresses existing gaps but also provides new solutions for mobile big data privacy protection, offering significant theoretical and practical value.

Big Data Privacy Protection and Security Provisions of the Healthcare SecPri-BGMPOP Method in a Cloud Environment

One of the industries with the fastest rate of growth is healthcare, and this industry’s enormous amount of data requires extensive cloud storage. The cloud may offer some protection, but there is no assurance that data owners can rely on it for refuge and privacy amenities. Therefore, it is essential to offer security and privacy protection. However, maintaining privacy and security in an untrusted green cloud environment is difficult, so the data owner should have complete data control. A new work, SecPri-BGMPOP (Security and Privacy of BoostGraph Convolutional Network-Pinpointing-Optimization Performance), is suggested that can offer a solution that involves several different steps in order to handle the numerous problems relating to security and protecting privacy. The Boost Graph Convolutional Network Clustering (BGCNC) algorithm, which reduces computational complexity in terms of time and memory measurements, was first applied to the input dataset to begin the clustering process. Second, it was enlarged by employing a piece of the magnifying bit string to generate a safe key; pinpointing-based encryption avoids amplifying leakage even if a rival or attacker decrypts the key or asymmetric encryption. Finally, to determine the accuracy of the method, an optimal key was created using a meta-heuristic algorithmic framework called Hybrid Fragment Horde Bland Lobo Optimisation (HFHBLO). Our proposed method is currently kept in a cloud environment, allowing analytics users to utilise it without risking their privacy or security.

A lattice-based efficient certificateless public key encryption for big data security in clouds

With the advancement of technology and the development of digitization, big data has become an integral component of modern society. Due to the huge volume of big data, users generally upload data to the cloud for storage and computation. Because data in the cloud is beyond the control of users, it faces threats such as data security and personal privacy leakage. To address these concerns and achieve big data security and privacy protection in clouds, data encryption is crucial. Traditional public key encryption involves complex certificate management in a public key infrastructure (PKI). Although the identity-based encryption (IBE) scheme avoids the management of public key certificates, it has issues with key escrows and identity revocation. The certificateless public key encryption (CL-PKE) not only solves the key escrows problem in IBE, but also maintains its advantage of not requiring the use of certificate in PKI, making it suitable for the security protection of big data in clouds. However, most CL-PKE schemes are constructed based on large integer factorization and discrete logarithm problems, which cannot resist quantum computing attacks. Therefore, this paper proposes an efficient lattice-based CL-PKE scheme for big data security in clouds. In addition, the proposed scheme is extended to a lattice-based certificateless proxy re-encryption scheme, facilitating secure big data sharing in clouds. The proposed CL-PKE scheme can resist attacks from both internal and external adversaries, and has been proven to be IND-CPA secure under the learning with errors (LWE) assumption. Experiments have shown that the proposed CL-PKE scheme has lower computational and communication costs. The proposed lattice-based schemes can effectively provide post quantum security for the security and privacy protection of big data in clouds.

Application of Big Data Privacy Protection Based on Edge Computing in the Prediction of Martial Arts Training Movement Trajectory

With the development of science and technology, edge computing and big data privacy protection are more and more widely used in various fields. The application of big data privacy protection based on edge computing in the prediction of sport trajectories for martial arts training shows good performance and privacy protection. Edge computing can process and analyze data in real time to improve the accuracy and efficiency of sport trajectory prediction. Big data privacy protection can ensure the security of athletes' personal information and training data and prevent data leakage and misuse. However, existing related works still have some deficiencies in data processing speed, accuracy, and privacy protection. In this paper, the authors address these issues and propose an edge computing-based big data privacy protection method to improve the accuracy and security of sport trajectory prediction for martial arts training.

Research on big data privacy protection technology

The extensive application of big data technology makes data burst with unprecedented value and vitality. However, due to the large amount of data, many data sources, and complex data access relationships, the current development of privacy protection technology is seriously lagging behind the development of big data technology, which restricts the application and promotion of big data. At present, it is urgent to sort out the development status of big data privacy protection technology, so as to provide a reference for the research and breakthrough of key issues of big data privacy protection. This paper analyzes k-anonymity and differential privacy protection, and points out that a protection method that can reduce the cost of high-dimensional data processing and ignore the background knowledge requirements of attackers is urgently needed for privacy protection in big data scenarios. At the same time, this paper also puts forward suggestions for the application and future development of these technologies.

Blockchain-Oriented Privacy Protection of Sensitive Data in the Internet of Vehicles

The Internet of Vehicles is the specific instantiation of the Internet of Things in the field of transportation. Vehicle and driving data are often used to mine information about people's lifestyles and preferences. Without advanced data encryption and strict authorization measures, personal privacy and security are at great risk. To solve the problem that the relatively low security of vehicles connected to the Internet may threaten the privacy of users, a blockchain-based sensitive data privacy-protection scheme for vehicles connected to the Internet is proposed. First, association rules are used to mine big data in the Internet of Vehicles. Then, a data security aggregation protocol is established. Finally, an end-to-end encryption mechanism is created. The privacy protection of big data in the Internet of Vehicles is achieved through static and dynamic strategies. The experimental results show that the scheme can improve the hiding rate of sensitive data, the key generation time and encryption time are shorter than those of typical technologies, and the encryption ability is better. Therefore, in protecting the privacy of big data in the Internet of Vehicles, the blockchain proposed in this paper has better privacy protection and encryption abilities and is applicable to the protection of sensitive data in the Internet of Vehicles.

Research on Personal Big Data Privacy Protection and Security Risk Prevention

Research on Personal Big Data Privacy Protection and Security Risk Prevention

Healthcare Big Data Privacy Protection Model Based on Risk-Adaptive Access Control

Edge computing is playing an increasingly important role in the field of health care. Edge computing provides high-quality personalized services to patients based on user and device data information. However, edge nodes will collect a large amount of sensitive patient information, and patients will also bear the risk of privacy disclosure while enjoying personalized services. How to reduce the risk of privacy disclosure while ensuring that patients enjoy personalized services brought by edge computing is the research content of this paper. In this paper, the work flow and management mode of Hospital Information System (HIS) are investigated on the spot, and the risk-adaptive access control model based on entropy is established. First, we use International Classification of Diseases, Tenth Revision (ICD-10) to mark the information resources accessed by users and use information entropy to measure the correlation “α” between medical information accessed by users and work tasks. Finally, we analyze the relationship between correlation “α” and risk through an example. The results show that users with high correlation α have low risk of access behavior, and users with low risk have high correlation α of access information resources and work goals. This discovery can help managers predict users’ access behavior in the Big Data environment, so as to dynamically formulate access control policies according to the actual access situation of users and then realize the privacy protection of medical big health data.

Big Data Privacy Protection Technology

With the development of Internet technology, we have ushered in the “big data era”. The arrival of big data has made data calculation, acquisition, and statistics more convenient, and has had a significant impact on people’s lives, social management, and national governance. However, because the information unearthed by big data analysis technology may exceed people’s imagination, its privacy issues have also attracted much attention. This paper first briefly introduces the definition and current status of big data and its privacy issues, describes the privacy protection link model, and then starts from the privacy protection technology of big data, introduces the K-anonymization traditional big data privacy protection technology, advantages and disadvantages, Specific algorithms, etc.

Research on Big Data Privacy Protection based on the Three-Dimensional Integration of Technology, Law, and Management

빅 데이터는 정보 이용자들에게 큰 이익을 가져다 줄뿐만 아니라 사용자의 개인 정보 유출에 큰 위험을 초래할 수 있다. 본 논문에서는 빅 데이터 환경에서 사용자 정보 프라이버시 유출 위험을 최소화하기 위해 빅데이터 프라이버시 보호 라이프 사이클 중 데이터 사용 단계를 중심으로 사용자 정보 프라이버시 보호 방식을 제시한다. 제시된 방법은 기술적, 법적, 관리적 등 3가지 측면에서 사용자 정보 프라이버시 보호를 위한 방식을 제안하고 제시된 방식이 데이터 사용 단계에서 주요 사용자 정보 프라이버시 취약점에 대응할 수 있는지 제시한다. 또한 빅데이터 환경의 사용자 정보 프라이버시 보호를 위해 진행된 기존 연구들과의 차이점을 분석하여 본 논문에서 제시된 기술적, 법적, 관리적 측면을 함께 고려한 보호 방식의 장점가 특징을 보인다.

RoD: Evaluating the Risk of Data Disclosure Using Noise Estimation for Differential Privacy

Differential privacy is a paradigm of big data privacy protection that offers protection even when an attacker has arbitrary background knowledge in advance. Consequently, it is viewed as a reliable protection mechanism for sensitive information. Differential privacy introduces noise, such as Laplace noise, to obfuscate the true values in a data set while preserving its statistical properties. However, a large amount of Laplace noise added into a data set is typically defined by the discursive scale parameter of Laplace distribution. The privacy budget $\varepsilon$ ɛ in differential privacy has been theoretically interpreted, but the implication on the risk of data disclosure ( RoD ) in practice has not yet been well studied. Moreover, choosing an appropriate value for $\varepsilon$ ɛ is not straightforward because it considerably affects the level of privacy in a data set. In this paper, we define and evaluate RoD in a data set with either numerical or binary attributes for numerical or counting queries with multiple attributes based on noise estimation. Through confidence probability of noise estimation, we provide a simple method to select the privacy budget $\varepsilon$ ɛ for differential privacy and associate differential privacy with $k$ k -anonymization. Finally, we show the relationship between the RoD and $\varepsilon$ ɛ as well as between $\varepsilon$ ɛ and $k$ k in our experimental results. To the best of our knowledge, this is the first study using the quantity of noise as a bridge to evaluate RoD for multiple attributes (either numerical or binary data) and determine the relationship between differential privacy and $k$ k -anonymization.

Analysis of Using Blockchain to Protect the Privacy of Drone Big Data

In order to solve the problem of privacy protection of unmanned aerial vehicle (UAV) big data, it is necessary to design a privacy protection scheme that guarantees safe sharing of UAV big data. In this work, blockchain technology is adopted to solve the privacy protection problem of UAV big data. In particular, the proposed privacy protection scheme uses a number theory research unit cryptosystem for encryption of blockchain data. Privacy analysis is provided to validate the security requirements. The performance evaluation results show that the proposed UAV big data privacy protection scheme based on blockchain technology has low computing cost in terms of key production, encryption, and decryption. It also outperforms the conventional approaches. This work aims to provide a guideline for future research on the privacy protection of UAV data.

MNSSp3: Medical big data privacy protection platform based on Internet of things

How to transform the growing medical big data into medical knowledge is a global topic. However, medical data contains a large amount of personal privacy information, especially electronic medical records, gene data and electroencephalography data; the current methods and tools for data sharing are not efficient or cannot be applied in real-life applications. Privacy disclosure has become the bottleneck of medical big data sharing. In this context, we conducted research of medical data from the data collection, data transport and data sharing to solve the key problems of privacy protection and put forward a privacy protection sharing platform called MNSSp3 (medical big data privacy protection platform based on Internet of things), which attempts to provide an effective medical data sharing solution with the privacy protection algorithms for different data types and support for data analytics. The platform focuses on the transmission and sharing security of medical big data to provide users with mining methods and realizes the separation of data and users to ensure the security of medical data. Moreover, the platform also provides users with the capacity to upload privacy algorithms independently. We discussed the requirements and the design components of the platform, then three case studies were presented to verify the functionality of the platform, and the results of the experiments show clearly the benefit and practicality of the proposed platform.

Big Data, Algorithms and Health Data

The most recent decade featured a data revolution in the healthcare sector in screening, monitoring and coordination of aid. Big data analytics have revolutionarized the medical profession. The health sector relys on Artificial Intelligence (AI) and robotics as never before. The opportunities of unprecedented access to healthcare, rational precision and human resemblance but also targeted aid in decentralized aid grids are obvious innovations that will lead to most sophisticated neutral healthcare in the future. Yet big data driven medical care also bears risks of privacy infringements and ethical concerns of social stratification and discrimination. Today’s genetic human screening, constant big data information amalgamation as well as social credit scores pegged to access to healthcare also create the most pressing legal and ethical challenges of our time. The call for developing a legal, policy and ethical framework for using AI, big data, robotics and algorithms in healthcare has therefore reached unprecedented momentum. Problematic appear compatibility glitches in the AI-human interaction as well as a natural AI preponderance outperforming humans. Only if the benefits of AI are reaped in a master-slave-like legal frame, the risks associated with these novel superior technologies can be curbed. Liability control but also big data privacy protection appear important to secure the rights of vulnerable patient populations. Big data mapping and social credit scoring must be met with clear anti-discrimination and anti-social stratification ethics. Lastly, the value of genuine human care must be stressed and precious humanness in the artifical age conserved alongside coupling the benefits of AI, robotics and big data with global common goals of sustainability and inclusive growth. The report aims at helping a broad spectrum of stakeholders understand the impact of AI, big data, algorithms and health data based on information about key opportunities and risks but also future market challenges and policy developments for orchestrating the concerted pursuit of improving healthcare excellence. Stateshuman and diplomates are invited to consider three trends in the wake of the AI (r)evolution: Artificial Intelligence recently gained citizenship in robots becoming citizens: With attributing quasi-human rights to AI, ethical questions arise of a stratified citizenship. Robots and algorithms may only be citizens for their protection and upholding social norms towards human-like creatures that should be considered slave-like for economic and liability purposes without gaining civil privileges such as voting, property rights and holding public offices. Big data and computational power imply unprecedented opportunities for: crowd understanding, trends prediction and healthcare control. Risks include data breaches, privacy infringements, stigmatization and discrimination. Big data protection should be enacted through technological advancement, self-determined privacy attention fostered by e-education as well as discrimination alleviation by only releasing targeted information and regulated individual data mining capacities. The European Union should consider establishing a fifth trade freedom of data by law and economic incentives: in order to bundle AI and big data gains large scale. Europe holds the unique potential of offering data supremacy in state-controlled universal healthcare big data wealth that is less fractionate than the US health landscape and more Western-focused than Asian healthcare. Europe could therefore lead the world on big data derived healthcare insights but should also step up to imbuing humane societal imperatives on these most cutting-edge innovations of our time.

D2D Big Data Privacy-Preserving Framework Based on (a, k)-Anonymity Model

As a novel and promising technology for 5G networks, device-to-device (D2D) communication has garnered a significant amount of research interest because of the advantages of rapid sharing and high accuracy on deliveries as well as its variety of applications and services. Big data technology offers unprecedented opportunities and poses a daunting challenge to D2D communication and sharing, where the data often contain private information concerning users or organizations and thus are at risk of being leaked. Privacy preservation is necessary for D2D services but has not been extensively studied. In this paper, we propose an (a, k)-anonymity privacy-preserving framework for D2D big data deployed on MapReduce. Firstly, we provide a framework for the D2D big data sharing and analyze the threat model. Then, we propose an (a, k)-anonymity privacy-preserving framework for D2D big data deployed on MapReduce. In our privacy-preserving framework, we adopt (a, k)-anonymity as privacy-preserving model for D2D big data and use the distributed MapReduce to classify and group data for massive datasets. The results of experiments and theoretical analysis show that our privacy-preserving algorithm deployed on MapReduce is effective for D2D big data privacy protection with less information loss and computing time.

Big data in precision medical research and the establishment of the privacy protection system

Objective Precision medical research is highly depended on the collection and analysis of big data from the new generation of life science. These data will inevitably contain a lot of sensitive information relating to individual privacy. This paper mainly investigates how to effectively integrate and share big data under the premise of protecting the privacy of participants and patients. Methods The international policies on precision medical big data and privacy protection were briefly summarized and analyzed. The establishment of the privacy protection system on big data in precision medical research was also explored, mainly from the aspects of technology research and development, ethical norms and policy construction. Results Several suggestions and strategies were put forward for the development of big data privacy protection system, including construction of ethical norms and policies, development of big data privacy protection technology, privacy protection education of participants and patients, fully transparent informed consent and some other related strategies. Conclusions These suggested strategies on the establishment of big data privacy protection system will provide references for promoting the establishment of related policies and regulations in our country. And this privacy protection system will certainly protect and promote the long-term healthy development of precision medicine. Key words: Precision medicine; Big data; Privacy protection; Medical ethics

A Big Data Privacy Protection Solution Based on Compressive Sensing Theory

This paper analyzes the progress of privacy protection in big data environment, and introduces the theory of compressive sensing, and then, proposes a new big data privacy protection method based on compressed sensing and discusses the realization of the method. According to the analysis, this new method has a certain reference value in the realization of big data privacy preserving technology.

A Privacy Protection Evaluation Mechanism for Dynamic Data Based on Chunk-Confusion

Nowadays big data security plays a major issue in cloud computing. Chunk-confusion-based privacy protection mechanism (CCPPM) protects the privacy of the tenants in plaintext. But both multi-tenant applications’ data and tenants’ privacy requirements are dynamically changing, which will have a great effect on the underlying storage model of cloud data. Moreover, the tenants’ business processing will change the data distribution and destroy the distribution balance of privacy data, which makes the data stored in the cloud face the risk of leakage of privacy. Therefore, the paper proposed a privacy protection evaluation mechanism for dynamic data based on CCPPM. The paper firstly introduces three kinds of the privacy leakages due to unbalanced data under the CCPPM, and analyzes two methods used for attacking. Aiming at the privacy leakages and the attack methods, we proposed a dynamic data processing algorithm to record the tenants’ operation sequence and set up the corresponding evaluation formula. Next, we evaluated the effect of privacy protection from two aspects of simple attack and background-knowledge-based attack, and used the data distribution similarity privacy preserving dynamic evaluation algorithm presented in this paper to obtain the measurement results of privacy leakages. Finally, according to the evaluation results, the defense strategies are given to prevent data privacy leakages. The experimental evaluation proves that rationality of dynamic the evaluation mechanism proposed in this paper has better feasibility and practicality for big data privacy protection.

An improved anonymity model for big data security based on clustering algorithm

SummaryThe accumulation of massive data generates the new concept of big data. The relationships hidden in big data can bring great benefits, which have attracted public attentions. Meanwhile, the challenges of big data security are also more serious than ever. Privacy disclosure is one of the most concerned problems, and the privacy protection of big data is more difficult than traditional information protection. The technology of data publishing anonymous protection can provide privacy protection with the respect of data releasing. K‐anonymity and L‐diversity are two kinds of anonymity model. Their main idea is to generalize the value of quasi‐identifier and make the data accord with the model. In this paper, we propose the improved model which integrate K‐anonymity with L‐diversity and can solve the problem of imbalanced sensitive attribute distribution. K‐member clustering algorithm can translate the problem of anonymity into the problem of clustering and find a set of equivalence classes in which the records will be generalized to the same value. We utilize K‐member clustering algorithm to realize the improved anonymity model which can reduce the algorithm execution time and information loss. The integration of anonymity model and clustering algorithm makes the generalization process more efficient, which is particularly important for big data. Copyright © 2016 John Wiley & Sons, Ltd.

Research and Practice of DataRBAC-based Big Data Privacy Protection

With the presentation of the era of big data, big data not only brings our life and production positive help and it also exposes us to uncertain risk. How to ensure a secure and private big data against current technical conditions is an ur- gent issue subject to answer. Facing the challenges of subscriber privacy protection and access control and others, the pa- per mainly studies the Role-Based Access Control (RBAC) and analyzes the demand for data item control from the point of view of data security and privacy protection and then raises an access control model based on data item and achieves the multiple control to the data item in the interface based on role when various subscriber operates same function. The practice application further proves its value.