Research on Classification and Desensitization Strategies of Sensitive Educational Data

In the context of the rapid development of digital education, the security of educational data has become an increasing concern. This paper explores strategies for the classification and grading of educational data, and constructs a higher educational data security management and control model centered on the integration of medical and educational data. By implementing a multi-dimensional strategy of dynamic classification, real-time authorization, and secure execution through educational data security levels, dynamic access control is applied to effectively enhance the security and controllability of educational data, providing a secure foundation for data sharing and openness.

An efficient perturbation approach for multivariate data in sensitive and reliable data mining

A Lot of documents in many organizations from companies to governments are shared on on-premise storage or clouds. And some of those documents may contain sensitive information such as names, social security numbers, addresses and so on. Especially a large amount of sensitive information written in Korean have been leaked nowadays. It can be severe problems to not only individuals but also many organizations. Therefore, for information protection, data loss prevention (DLP) has been needed. DLP systems based on pattern matching were popular in the past. But they have a difficulty handling new type of sensitive data whenever they come. To handle this problem, sensitive data identification with NER is proposed as a useful method of DLP system. By using NER, we can classify the words in a document into categories which consist of name, location and so on. These categories are considered as sensitive information. This approach shows good performance identifying information in unstructured data(e.g. sentences) which have contextual information whereas it has a weakness identifying sensitive information in structured data (e.g. personal names in cells of the table). Actually, a large amount of sensitive information is organized in structured data and the form of structured data varies depending on the document. Furthermore, it also has difficulties identifying data written in Korean because of its characteristics. We proposed a primary preventive measure of DLP by identifying sensitive data in tables of Korean documents combining text generation and NER models regardless of the form of tables and masking them as to share documents without disclosing sensitive information.

Mobile apps are widely used and often process users’ sensitive data. Many taint analysis tools have been applied to analyze sensitive information flows and report data leaks in apps. These tools require a list of sources (where sensitive data is accessed) as input, and researchers have constructed such lists within the Android platform by identifying Android API methods that allow access to sensitive data. However, app developers may also define methods or use third-party library’s methods for accessing data. It is difficult to collect such source methods, because they are unique to the apps, and there are a large number of third-party libraries available on the market that evolve over time. To address this problem, we propose DAISY, a Dynamic-Analysis-Induced Source discoverY approach for identifying methods that return sensitive information from apps and third-party libraries. Trained on an automatically labeled dataset of methods and their calling context, DAISY identifies sensitive methods in unseen apps. We evaluated DAISY on real-world apps, and the results show that DAISY can achieve an overall precision of 77.9% when reporting the most confident results. Most of the identified sources and leaks cannot be detected by existing technologies.

Efficient perturbation techniques for preserving privacy of multivariate sensitive data

DeepSeek is reshaping the higher education model in all aspects by assisting differentiated teaching and personalized learning, promoting flat teaching management processes, creating interactive learning modes with deep participation, creating intelligent paths for boundaryless learning, providing accurate and comprehensive data-driven feedback, and enhancing global education equity and inclusiveness. For better applying artificial intelligence to promote the development of higher education, this study summarizes the main scenarios in which DeepSeek R1 contributes to the sustainable development of higher education. The development strategy of higher education in the era of intelligence is obtained, which includes improving teacher literacy we should continue to promote the healthy and innovative development of education models, actively deepen artificial intelligence to assist in the construction of teacher teams, enhance the interpretability of education models, improve the precision support of personalized learning, real-time detect and regulate student emotions, construct a scientific evaluation system for large models, bridge the global education digital divide, establish an ethical and moral framework for artificial intelligence, strengthen the cross-cultural adaptability of intelligent collaboration, and help lifelong learning and sustainable social development.

The Finnish Biodiversity Information Facility FinBIF receives, stores and manages biodiversity data mobilised in Finland, and shares the data through its own portal (species.fi) and through Global Biodiversity Information Facility GBIF. FinBIF’s data policy (data policy) embraces the European FAIR data principles (FAIR - Findable, Accessible, Interoperable, Reusable; Wilkinson (2016)) but also incorporates specific restrictions stemming from national legislation, researchers’ needs, and data owners’ requirements. Here, we describe how the necessary, due to various reasons from sensitivity of the data to research embargo, restrictions to openness have been defined and implemented on the policy level and in technical data infrastructure solutions. We hope to contribute to an improvement of data management in the international biodiversity data infrastructures. In Finland, the law prohibits public authorities from distributing occurrence data if this causes increased threat to endangered species. However, neither the definition of ‘endangered species’ nor guidelines for the evaluation of potential risk by openness of data are formulated. To enable mobilisation of datasets containing information on endangered species, FinBIF convened a task force commissioned to set rules on data distribution, which respect the spirit of the law. The task force consisted of representatives of relevant data holding authorities and it consulted a wide group of taxon experts and the species information community. First, a list of species, judged to be among those targeted by the spirit of the law, was created (sensitive species data). Then the rules of restriction were decided on for each of the species. Measures of restriction ranged from complete non-disclosure of data to temporal and spatial restrictions. The identified safeguards concerning the sensitive data management in all use cases led us to create a series of innovative solutions Researchers often wish to restrict the openness of data they have gathered for research purposes These restrictions include embargo periods, limitations on the precision of data and controls on how the data is used. In many cases, however, researchers are willing to allow unrestricted official use of their data in certain cases such as for conservation management or land use planning. In these cases they will often allow storage and restricted use of exact data without an embargo. The same may be true for other data owners, such as nongovernmental organisations (NGOs) or private citizens. To support restrictions to openness, while simultaneously securing mobilisation of valuable datasets, FinBIF applies data sharing contracts including, as a rule, a precondition to share the original data with the public authorities for official use under the Creative Commons 4.0 BY -licence (CC 4.0 BY). The technical solution to enabling the rather complex data policy is that FinBIF stores the collated data in two separate data warehouses: a public one for the distribution of fully open data and temporally and spatially coarsened sensitive data, alongside another containing all data but with restricted access to authorised users. In addition, to allow case-by-case release of restricted data, FinBIF has developed a data request function (Fig. 1). When users of the open data retrieve a dataset using, e.g., taxonomic and spatial filtering, they receive a search result stating whether there are restricted data available based on the filters used. In these cases a user can issue a data request, automatically distributed to all owners of data contained in the collated data batch. Agreeing on the principles about how to apply restrictions to data openness and how to define authoritative use, has not been easy given the lack of precedents. It has required thorough and inclusive consultation with both state administration, conservation practitioners, scientific specialists and lawyers. The two main cultural constraints to overcome have been (1) embracing the FAIR principles of truly ‘as open as possible’ and only ‘as closed as (absolutely) necessary (European Commission 2016); and, perhaps surprisingly, (2) figuring out novel ways to work across different state administrative sectors to share data.

In the age of the Internet of Things (IoT), an immense volume of data is rapidly generated and entrusted to external units, including cloud centers for storage and analysis. This implies that the data produced by IoT devices is conveyed and stored in a distant location, where it can undergo processing, examination, and utilization to extract valuable insights and facilitate informed decision-making. Privacy protection methods can be applied in IoT settings to safeguard sensitive and private information from potential security risks. Within the scope of IoT privacy, data sanitization involves the permanent and irreversible concealment of all sensitive data from extensive streams of information. Conversely, data restoration is the reverse process of data sanitization. Considering privacy preservation in IoT for data streaming, this research paper implements a Deep Learning (DL) tuned key-based data sanitization and restoration approach. The entire process of the implemented approach is handled by two significant steps such as data sanitization and data restoration. In the data sanitization step, three processes take place such as retrieval of features, key generation via Self-Adaptive Osprey Optimization (SAOO) and key tuning via Enhanced Convolutional Neural Network (ECNN). Initially, features such as raw features, statistical features and improved entropy-based features are retrieved (considering this as sensitive information. To sanitize this, key generation is the subsequent phase, where optimal keys are generated by SAOO and its security and confidentiality are ensured by the Khatri–Rao product. Then, these optimal keys are tuned by ECNN to ensure the efficacy of privacy preservation in IoT without compromising the performance of the model. Finally, the data restoration process is to recover the previously sanitized data in an IoT environment and reconstitute the original data from their sanitized form while maintaining the security and confidentiality of raw sensitive data. With these steps, the privacy of an IoT environment is protected from any form of issues. Additionally, this approach is experimentally explored to determine its efficacy using various analyses.

Now-a-days there are different types of cybercrime, Phishing is one of cyber-attacks where attackers impersonate as a member of legitimate institutions or organizations through an email, text message, advertisements or through any means to steal sensitive information which results to loss of personal and sensitive information such as account no, social security no, credit card no etc.. Phishing attack has been increasing exponentially. In this attack mostly innocent users are comes to losses their sensitive, unique, personal, valuable and secure data and information’s. Many hackers are accomplished through phishing attacks where client are trapped into interacting with web-pages which looks like to be legitimate websites. The websites exactly seems to be semantically as well as visually to the original websites. The main idea of the phisher or hackers is to gain and purloin the critical information such as credential account, username, password and other private information related to any organization and company. According to phishing or web spoofing techniques is one examples of social engineering attack. Phishers are appears in many platform of communication such as in the form of VOIP, message and e-mails which is not real. Commonly users have many accounts on various websites including social media, email, and also in bank. So that innocent users are the most vulnerable targets for these types of attack. This happened because most of the peoples are unknown of the sensitive data, which helps them to get their information successfully. As of 2020, phishing is the most common attack performed by cyber criminals according to FBI’s Internet Crime Complaint Centre. First phishing Datasets are collected from phish tank and then legitimate websites are collected from University of New Brunswick and then dataset is preprocessed using wrapper and filter method so that it covers the dataset which gets missed, tampered and unstructured.

The rapid development of information technology makes business organizations face major challenges in managing and securing sensitive data. The development of information technology has changed data storage systems from local media to cloud computing, enabling data access and processing via the internet. Services like Google Drive and OneDrive offer security, accessibility, and data backup, but pose challenges for business organizations, especially in terms of security of sensitive data and operational costs that increase as users grow. This research aims to design a local cloud as a private cloud to improve data management and security in the organization. With a private cloud, companies can control data access more tightly, increase privacy, and enable employees to access applications and data from various locations more safely and efficiently. The results of this research are in the form of a private cloud based on a private server which was developed as a file server with access rights based on departments, across departments, projects or work groups. This system utilizes open-source technology and is equipped with an encrypted Virtual Private Network (VPN) to support remote work models (Work From Home, WFH) and work flexibility from anywhere (Work From Anywhere, WFA). With this solution, organizations can increase operational efficiency, reduce the risk of data leaks, optimize more affordable technology resources, and maintain full control of sensitive company data.

The financial institutions provide the resources to protect their sensitive data and information by trying to prevent unauthorized leakage. They approve policies and realize technical restrictions to block the loss and revelation of sensitive data and information by external attackers as well as careless insiders. One example of Data Loss Prevention (DLP) restrictions consists of endpoint protection solutions to block data transmissions to USB storage devices. Nevertheless, financial institutions approve exceptions to these policies, based on the business need for the specific user, in order to be able to fulfill their job-related tasks. But from these exceptions derive the following questions: How an approval for an exception can create impact over the risk of data leakage for the financial institution? What is the particular risk for according an individual user a confident exception? This paper introduces a new concept to risk depending on exception management, which will provide the financial institution to assign exceptions derived from on basic DLP. Initially, the paper presents an approach for evaluating and classification users based on their access to sensitive data and information, and afterward, a standard of rights is decided for assigning exceptions to derive from the classification of users, which allows specific approvers to prepare knowledgeable decisions concerning exception requests.

Current power sensitive data security protection adopts classification and grading protection. Company classification and grading are mainly in formulating specifications. Data classification and grading processing is carried out manually, which is heavy and time-consuming, while traditional data identification mainly relies on rules for data identification, the level of automation and intelligence is low, and there are many problems in recognition accuracy. Data classification and classification is the basis of data security protection. Sensitive data identification is the key to data classification and classification, and it is also the first step to achieve accurate data security protection. This paper proposes an intelligent identification and tracking technology of sensitive data for electric power big data, which can improve the ability of data classification and classification, help the realization of data classification and classification, and provide support for the accurate implementation of data security capabilities.

In February 2020, the CPC Central Committee defined the epidemic at a meeting which with the theme of "Coordinating the epidemic prevention and control with economic and social development”—“The COVID-19 outbreak is a major public health emergency that has spread in the fastest speed, caused the most extensive infection and is the most difficult to contain in the country since the founding of the People's Republic of China”. From this definition, it can be seen that the impact of this epidemic on China is very huge. Sudden outbreak of the COVID-19 inevitably deal a relatively big blow to China's economic and social development. How to adopt effective measures to promote economic and social recovery and development under the working on an ongoing basis to contain the virus is an urgent issue that needs to be resolved. This article takes the Coastal Economic Zone of Guangdong Province as a research object. Analyzes the impact of the epidemic on it, and the difficulties and problems facing the current economic development. In response to these problems, this article makes several suggestions: continued prevention and control of the epidemic situation, increased government support, optimized industrial structure, and promoted economic growth by the Troika. So as to promote Guangdong's economic recovery and further promote Guangdong's economic and social development.

Wavelet based steganographic technique to protect household confidential information and seal the transmitted smart grid readings

The adoption of blockchain technology in prevalent applications has substantially increased the use of blockchain wallets. Even though consensus, cryptography, and decentralization make blockchain data structures pretty safe, it’s still hard to protect sensitive data at endpoints because each new block of data connects to all the previous blocks in a nearly unbreakable way. Thus, we seek to improve upon data security and transaction ease. Blockchain wallet store most of the users’ sensitive data in the form - namely initial-seed, private, public keys and other sensitive asset information. In order, we propose to attain sensitive data protection using an air-gapped hardware wallet and transactional privacy by hashing the transaction with the blake3 algorithm. More precisely, the mechanism consists of a credit card-sized air- gapped hardware wallet owned only by each party. Along with the wallet, a mobile app ‘‘Crypto-Trans’’ for certificate verification is introduced which acts as an interface between the hardware wallet and the blockchain. Last but not least, the proposed wallet is compared to existing wallets on key parameters to establish its relative superiority in terms of effectiveness and efficiency.