Privacy Of User Data Research Articles

Federated-Learning-Based Anomaly Detection for IoT Security Attacks

The Internet of Things (IoT) is made up of billions of physical devices connected to the Internet via networks that perform tasks independently with less human intervention. Such brilliant automation of mundane tasks requires a considerable amount of user data in digital format, which, in turn, makes IoT networks an open source of personally identifiable information data for malicious attackers to steal, manipulate, and perform nefarious activities. A huge interest has been developed over the past years in applying machine learning (ML)-assisted approaches in the IoT security space. However, the assumption in many current works is that big training data are widely available and transferable to the main server because data are born at the edge and are generated continuously by IoT devices. This is to say that classic ML works on the legacy set of entire data located on a central server, which makes it the least preferred option for domains with privacy concerns on user data. To address this issue, we propose the federated-learning (FL)-based anomaly detection approach to proactively recognize intrusion in IoT networks using decentralized on-device data. Our approach uses federated training rounds on gated recurrent units (GRUs) models and keeps the data intact on local IoT devices by sharing only the learned weights with the central server of FL. Also, the approach’s ensembler part aggregates the updates from multiple sources to optimize the global ML model’s accuracy. Our experimental results demonstrate that our approach outperforms the classic/centralized machine learning (non-FL) versions in securing the privacy of user data and provides an optimal accuracy rate in attack detection.

A Metaheuristic Approach for Encrypting Blockchain Data Attributes Using Ciphertext Policy Technique

Unlike public chains, the Alliance Blockchain Hyperledger Fabric has a member management service mechanism that may provide data isolation security at the channel level. However, because this data isolation security technique synchronizes plaintext data inside the channel, data leakage is still a possibility. Furthermore, in some fine-grained privacy protection circumstances, channel-based data access restriction is ineffective. In order to solve the data privacy security problems in the above-mentioned consortium chain superledger, a blockchain data attribute encryption scheme based on ciphertext policy is proposed. Combining the original Fabric Certificate Authority module in the Hyperledger, the proposed scheme can realize the user-level fine-grained security access to control blockchain data while also realizing the secure distribution of user attribute keys in the blockchain data attribute encryption scheme based on the ciphertext policy scheme. The security analysis of the scheme shows that the scheme achieves the security goals of attribute-based encryption user attribute private key secure distribution and data privacy protection. The scope of this research is that this study confirms that the solution’s architecture achieves fine-grained access control of private data on the Hyperledger Blockchain network and also the security objectives of secure transmission of user characteristic secret keys and data privacy protection. The performance analysis part also shows that the proposed scheme has good usability.

The Secure Lattice-Based Data Aggregation Scheme in Residential Networks for Smart Grid

With the rapid development of computer networks, the network architecture has been more and more complex and diverse. Smart grid is a kind of wireless sensor network consuming energy in smart city. Smart grid communications have developed rapidly in recent years, while the privacy, integrity, and confidentiality of user data have been the main security issues. However, lattice-based homomorphic cryptography can resist quantum attacks and effectively provide the security of the scheme. In this article, we proposed an aggregate signature scheme based on new Batch RSA. After that, a data aggregation scheme is proposed based on this aggregate signature scheme and the qualified homomorphic cryptosystem. Compared to the previous schemes, our scheme is more secure because we implement seven properties, such as post quantum, privacy, and so on. Compared to the representative scheme proposed by Abdallah <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">et al.</i> , our scheme has lower computation and communication overhead. In addition, the cryptographic algorithms involved in our scheme are lightweight. The network model in our scheme is suitable for large intelligent communities. So, our scheme is very suitable for the residential area network.

Public Key Encryption with Authorized Equality Test on Outsourced Ciphertexts for Cloud-Assisted IoT in Dual Server Model

In cloud computing, the outsourced data face many privacy and security threats. To allow the cloud server to perform comparison, search, and classification on outsourced ciphertexts while simultaneously providing privacy guarantee, the encryption method that supports the ciphertext equality test is considered as a promising way. Users are able to authorize the cloud server to conduct the ciphertext equality test, so that two ciphertexts can be determined whether they encrypt the same message without being decrypted. In this process, users do not need to retrieve, decrypt, and then perform comparison on data; thus, the computing and communication efficiency can be greatly improved, and the privacy of user data can be guaranteed at the cloud server side. However, existing encryption schemes supporting authorized ciphertext equality test in the single server model cannot resist the keyword guessing attacks, and the solutions in the dual server model do not provide simultaneous authorization on two servers. To address these issues, this paper proposes a public key encryption scheme supporting authorized equality test on ciphertexts in the dual server model (PKE-AUT), where the primary server and secondary server must get the authorization from users before performing a sequential equality test on ciphertexts. Security and performance analysis demonstrate that the proposed PKE-AUT scheme not only guarantees the privacy of user data and authorization but also is practical in cloud-assisted IoT-related applications.

A Blockchain-Based Privacy Protection Application for Logistics Big Data

Logistics business is generally managed by logistics orders in plain text, and there is a risk of disclosure of customer privacy information in every business link. In order to solve the problem of privacy protection in logistics big data system, a new kind of logistics user privacy data protection scheme is proposed. First of all, an access rights management mechanism is designed by combining block chain and anonymous authentication to realize the control and management of users' access rights to private data. Then, the privacy and confidentiality protection between different services is realized by dividing and storing the data of different services. Finally, the participants of the intra-chain private data are specified by embedding fields in the logistics information. The blockchain node receiving the transaction is used as the transit node to synchronize the intra-chain privacy data, so as to improve the intra-chain privacy protection within the business. Experimental results show that the proposed method can satisfy the privacy requirements and ensure considerable performance.

Проблема захисту обміну даними між мікропроцесорними пристроями в системах ІоТ

The article analyzes the methods of countering botnets in IoT systems. Today, the Internet of Things has become a popular term to describe scenarios in which Internet connectivity and computing power are spread across a multitude of objects, devices, sensors, etc. The main concept of IoT is the ability to connect all kinds of objects (things) that a person can use in everyday life. These objects (things) must be equipped with built-in sensors or sensors that have the ability to process information coming from the environment, exchange it and perform certain actions depending on the received information. The current lack of standards for the protection of such autonomous networks somewhat slows down the introduction of the Internet of Things into everyday life, so there are numerous vulnerabilities in the rapidly growing field of IoT technologies, which are used all over the world. Information protection and confidentiality is one of the priority components when choosing a certain system. Therefore, without adequate confidence in the security and privacy of user data, the IoT system will be uncompetitive. The Internet of Things can cause huge changes in everyday life, bringing a whole new level of comfort to ordinary users. But if the elements of such a system are not properly protected from unauthorized intervention, with the help of a reliable cryptographic algorithm, they will bring harm instead of good, giving cybercriminals a loophole to undermine information security. Since devices with built-in computers store a lot of information about their owner, including the ability to know their exact location, access to such information can help criminals commit a crime. To date, the level of success of botnet countermeasures depends mainly on organizational and political general conditions. Given that the establishment of cooperation or diplomatic agreements takes time, it can be concluded that the establishment of appropriate relations that legitimize cooperation for joint action is not suitable as an ad hoc scheme to combat current attacks. The situation is aggravated, given that modern botnet infrastructures are not under the responsibility of a single entity. In contrast, distributed peer-to-peer networks operate worldwide, so shutting down local parts (often no more than single computers) is not an effective solution. In general, countermeasures that require close cooperation are generally unfeasible today for both technical and political reasons Experts believe that prosecuting botnet developers is unlikely to have a strong impact on the global threat. Instead, botnets need to be fought on a technical level. Proactive measures should be taken by joint efforts of international security groups together with pro-government structures.

Data protection and threat prediction in industrial IoT cloud environment

There are several cloud computing data protection systems depending on accessibility control, attribute-based encryption (ABE), security and reputations, but still, it is disjointed and lacks a cohesive rationale. I-IoT systems including data protection and threat security metrics are discussed in this paper. I-IoT layered method that is general and extended, with privacy, security features and layers detection is proposed. To protect the user's private data, security procedures and crucial management events are established amongst all the lower, moderate and higher levels. To permit data flow across the levels of the proposed cloud computed powered I-IoT paradigm, we built security certificates. The proposed systematic method eliminates potential security vulnerabilities and is also utilised in conjunction with the optimal security approaches to mitigate the security threats on cloud computed decentralised industrial IoT systems and devices. The assaults toward I-IoT systems are described in this paper, and a detailed examination of remedies to these attacks.

Privacy preserving data mining - past and present

Data mining is the process of discovering patterns and correlations within the huge volume of data to forecast the outcomes. There are serious challenges occurring in data mining techniques due to privacy violation and sensitive information disclosure while providing the dataset to third parties. It is necessary to protect user's private and sensitive data from exposure without the authorisation of data holders or providers when extracting useful information and revealing patterns from the dataset. Also, internet phishing gives more threat over the web on extensive spread of private information. Privacy preserving data mining (PPDM) is an essential for exchanging confidential information in terms of data analysis, validation, and publishing. To achieve data privacy, a number of algorithms have been designed in the data mining sector. This article delivers a broad survey on privacy preserving data mining algorithms, different datasets used in the research and analyses the techniques based on certain parameters. The survey is highlighted by identifying the outcome of each research along with its advantages and disadvantages. This survey will guide the future research in PPDM to choose the appropriate techniques for their research.

Challenges of Privacy in Cloud Computing

Now, cloud computing has grown from being a business concept to one of the fastest-growing segments of the information technology industry. Cloud computing is a group of resources, hardware, software, and services offered through the Internet or the network. Cloud also consists of five important characteristics, three service models and four deployment models. From the consumers’ perspective, concerns about cloud computing security remain the essential inhibitors for cloud computing services, precisely related to the security and privacy issues of data in the cloud. This essay will go over. These difficulties include user data loss, data leakage, and privacy disclosure. A variety of cloud computing and service models are applied to boost an organization’s investment returns, profit, and financial growth. For companies or governmental agencies to operate on the cloud, cloud computing offers a suitable environment and additional benefits or advantages. As a result, it has a number of problems with data security and privacy. The user’s data are kept off-site and maintained by third parties. The consumers of cloud computing might be happy if the data are shielded from illegal access and misuse. Organizations and people who use the cloud are affected by the failure in data protection, which results in many challenges and problems, such as data theft. Concerns about privacy are also developing as “cloud computing” develops quickly and expands. For Open Systems and Internet applications, privacy has remained a touchy subject; the cloud suffers when discussing privacy. Adopting cloud computing services is only difficult because of privacy and security concerns. The challenges and solutions for cloud users and providers related to privacy and security are covered in this article. The document will provide researchers and security experts with information on privacy and security difficulties and solutions.

Hadoop Distributed File System Security Challenges and Examination of Unauthorized Access Issue

Hadoop technology is followed by some security issues. At its beginnings, developers paid attention to the development of basic functionalities mostly, and proposal of security components was not of prime interest. Because of that, the technology remained vulnerable to malicious activities of unauthorized users whose purpose is to endanger system functionalities or to compromise private user data. Researchers and developers are continuously trying to solve these issues by upgrading Hadoop’s security mechanisms and preventing undesirable malicious activities. In this paper, the most common HDFS security problems and a review of unauthorized access issues are presented. First, Hadoop mechanism and its main components are described as the introduction part of the leading research problem. Then, HDFS architecture is given, and all including components and functionalities are introduced. Further, all possible types of users are listed with an accent on unauthorized users, which are of great importance for the paper. One part of the research is dedicated to the consideration of Hadoop security levels, environment and user assessments. The review also includes an explanation of Log Monitoring and Audit features, and detail consideration of authorization and authentication issues. Possible consequences of unauthorized access to a system are covered, and a few recommendations for solving problems of unauthorized access are offered. Honeypot nodes, security mechanisms for collecting valuable information about malicious parties, are presented in the last part of the paper. Finally, the idea for developing a new type of Intrusion Detector, which will be based on using an artificial neural network, is presented. The detector will be an integral part of a new kind of virtual honeypot mechanism and represents the initial base for future scientific work of authors.

FL-NoiseMap: A Federated Learning-based privacy-preserving Urban Noise-Pollution Measurement System

AbstractIncreasing levels of noise pollution in urban environments are a primary cause of various physical and psychological health issues. There is an urgent requirement to manage environmental noise by assessing the current levels of noise pollution by gathering real-world data and building a fine-granularity real-time noise map. Traditionally, simulation-based, small-scale sensor-network-based, and participatory sensing-based approaches have been used to estimate noise levels in urban areas. These techniques are inadequate to gauge the prevalence of noise pollution in urban areas and have been shown to leak private user data. This paper proposes a novel federated learning-based urban noise mapping system, FL-NoiseMap, that significantly enhances the privacy of participating users without adversely affecting the application performance. We list several state-of-the-art urban noise monitoring systems that can be seamlessly ported to the federated learning-based paradigm and show that the existing privacy-preserving approaches can be used as an add-on to enhance participants’ privacy. Moreover, we design an “m-hop” application model modification approach for privacy preservation, unique to FL-NoiseMap. We also describe techniques to maintain data reliability for the proposed application. Numerical experiments on simulated datasets showcase the superiority of the proposed scheme in terms of users’ privacy preservation and noise map reliability. The proposed scheme achieves the lowest average normalized root mean square error in the range of 4% to 7% as the number of participants varies between 500 and 5000 while providing maximum coverage of over 95% among various competing algorithms. The proposed malicious contribution removal framework can decrease the average normalizedroot mean square error by more than 50% for simulations having up to 20% malicious users.

Research on Data Sharing Access Control Based on Blockchain Technology

Blockchain is now widely used in various industries due to its openness, transparency, autonomy, imtamability, decentralization, and traceability. With the wide application of blockchain technology, its security problems have also brought significant challenges, seriously affecting the implementation of blockchain applications. Data on the traditional blockchain is open to the entire network node. The update operation of data records is also transparent, accompanied by the leakage of user information. Although blockchain has the characteristics of anonymity and privacy, with its development, it has not been easy to meet users' needs. How to effectively protect user data privacy, realize data sharing on the blockchain, and ensure the security of data integrity, transmission efficiency, storage efficiency, and application is very important to break through the bottleneck of the development of blockchain technology and promote its application. This paper proposes a traffic data resource access control scheme based on blockchain technology by combining the attribute encryption access control system based on ciphertext strategy based on blockchain's distributed ledger technology and the openness and imtamability it brings. In this scheme, the task of attribute encryption is to ensure the security involved in the process of plaintext and ciphertext conversion and the efficiency of data sharing, and to implement a one-to-many access sharing policy, to enhance the safety of the access sharing system and improve the efficiency of the access control module. This paper introduces the access control mechanism from two aspects of system architecture and the access sharing process. Then the attribute encryption mechanism is designed. The design process includes symbol description, primary structure, algorithm design idea and algorithm construction. Finally, the functionality and security of the access control mechanism are analysed.

The Urgency of Doxing on Social Media Regulation and the Implementation of Right to Be Forgotten on Related Content for the Optimization of Data Privacy Protection in Indonesia

Data privacy that attached to every social media user has become a target of crime. One of the crime types that utilizes social media is doxing. Nowadays, the cases of doxing are increasing. There are still no specific and comprehensive normative rules that cover the data privacy protection to avoid doxing on social media. The fact makes the law enforcement still not optimal. This study is a descriptive study to answer some questions. Firstly, how to regulate doxing on social media based on the perspective of Indonesian law compared to the perspectives of other states in similar issue? Secondly, how the implementation of the right to be forgotten in doxing cases can optimize data privacy protection in Indonesia? This study used a normative juridical and case study approach. This study has resulted several results. Firstly, Indonesia needs special regulation for doxing on social media to protect the user data privacy. Secondly, the regulation of right to be forgotten should be reformulated and must be applied as a solution to doxing content. Doxing on social media regulation with the right to be forgotten can be further regulated through the legal regulation to provide a better data privacy protection.

Blockchain-Enabled Federated Learning for UAV Edge Computing Network: Issues and Solutions

Unmanned aerial vehicles (UAVs) extend the traditional ground-based Internet of Things (IoT) into the air. UAV mobile edge computing (MEC) architectures have been proposed by integrating UAVs into MEC networks during the current novel coronavirus disease (COVID-19) era. UAV mobile edge computing (MEC) shares personal data with external parties (such as edge servers) during intelligent medical analytics. However, this technique raises privacy concerns about patients’ health data. More recently, the concept of federal learning (FL) has been set up to protect mobile user data privacy. Compared to traditional machine learning, federated learning requires a decentralized distribution system to enhance trust for UAVs. Blockchain technology provides a secure and reliable solution for FL settings between multiple untrusted parties with anonymous, immutable, and distributed features. Therefore, blockchain-enabled FL provides both theories and techniques to improve the performance of intelligent UAV edge computing networks from various perspectives. This survey begins by discussing the current state of research on blockchain and FL. Then, compare the leading technologies and limitations. Second, we will discuss how to integrate blockchain and FL into UAV edge computing networks and the associated challenges and solutions. Finally, we discuss the fundamental research challenges and future directions.

A Decentralized Solution for Epidemiological Surveillance in Campus Scenarios

Throughout the various containment phases of a pandemic, such as Covid-19, digital tools and services prove to be essential measures to counteract its disrupting effects in social and working interactions. Within this scope, Nausica@DApp, the comprehensive solution proposed in this paper, is capable of facilitating a manageable co-existence of the teaching and research activities of a campus corporation with the organizational constraints posed by the pandemic, or even when the virus will exhibit an endemic behavior, throughout several intervention areas, such as personnel contact tracing, crowd gathering surveillance, and epidemiological monitoring. These operational requirements, in particular indirect contact tracing and overcrowd monitoring, call for the adoption of an absolute device localization paradigm, which, in the proposed solution, has been devised on top of the campus WiFi infrastructure and has proved to be encouragingly accurate in most cases. Absolute localization, on the other hand, imply a certain degree of server-based centralized operations, which might affect the preservation of user data privacy. The novelty of the proposed solution consists in maximizing confidentiality and integrity in sensitive personal information in spite of the centralized localization system, by means of a mostly decentralized approach, with mobile apps performing contact tracing matching operations locally to the portable devices on pseudonymized user data, whose authenticity is guaranteed by a blockchain which preserves their cryptographic hashes. Furthermore, the proposed novel solution includes enforcing privacy preservation by avoiding the use of the Bluetooth app-to-app channel for user data exchange from the overall design, conversely a typical approach of most contract tracing solutions. By means of a sensible use of the blockchain features, integrated into the solution’s microservice-based backend, a higher degree of operation transparency to rely on boosts the user’s level of trust. As a result of using the proposed solution, the availability and reliability of data about people gathering within the campus premises are enhanced; moreover, contact tracing is achieved while using the WiFi services, hence without forcing users to adopt new habits, and without compromising privacy. The overall system has been analysed in terms of performance and costs, and the experiments have shown that its adoption is viable and effective.

Ciphertext-Policy Attribute-Based Encryption for Cloud Storage: Toward Data Privacy and Authentication in AI-Enabled IoT System

People can store their data on servers in cloud computing and allow public users to access data via data centers. One of the most difficult tasks is to provide security for the access policy of data, which is also needed to be stored at cloud servers. The access structure (policy) itself may reveal partial information about what the ciphertext contains. To provide security for the access policy of data, a number of encryption schemes are available. Among these, CP-ABE (Ciphertext-Policy Attribute-Based Encryption) scheme is very significant because it helps to protect, broadcast, and control the access of information. The access policy that is sent as plaintext in the existing CP-ABE scheme along with a ciphertext may leak user privacy and data privacy. To resolve this problem, we hereby introduce a new technique, which hides the access policy using a hashing algorithm and provides security against insider attack using a signature verification scheme. The proposed system is compared with existing CP-ABE schemes in terms of computation and expressive policies. In addition, we can test the functioning of any access control that could be implemented in the Internet of Things (IoT). Additionally, security against indistinguishable adaptive chosen ciphertext attacks is also analyzed for the proposed work.

Wireless network security game based on conditional privacy policy

With the development of Internet technology, wireless body area network technology is proposed to improve people’s scientific and technological information level in this environment. Since the information collected and processed by wireless body area network is the privacy data of users, security and privacy are the focus of wireless body area network research. In this paper, a scheme to establish a variable radius mixing region is proposed. This scheme obtains the combined communication range by analyzing the user privacy conditions after the hybrid zone is established. According to the different conditions of users, a variable radius dynamic hybrid zone was established to maximize benefits. After the establishment of the hybrid zone is initiated, the members in the zone use the temporary symmetric secret key obtained from the unit for encrypted communication to ensure the demand for real-time message transmission. And finally an anonymous change protocol is proposed. The experimental data prove the feasibility of the renaming scheme of the mixed zone. Compared with other methods, the computation and traffic of this protocol are increased by 7% and 9 % respectively. The validation efficiency of batch validation algorithm is 93 %. The scheme in this paper is more flexible than the establishment of other fixed mixed zones, and the accuracy of this scheme is more accurate than the establishment of general dynamic mixed zones. Finally, it is proved that this scheme not only meets the conditional privacy policy but also uses the model to establish a dynamic hybrid area, which has more accurate accuracy and lower computational consumption, and plays an important role in the security protection of network privacy.

Design and Development of a Blockchain-Based System for Private Data Management

The concept of blockchain was introduced as the Bitcoin cryptocurrency in a 2008 whitepaper by the mysterious Satoshi Nakamoto. Blockchain has applications in many domains, such as healthcare, the Internet of Things (IoT), and data management. Data management is defined as obtaining, processing, safeguarding, and storing information about an organization to aid with making better business decisions for the firm. The collected information is often shared across organizations without the consent of the individuals who provided the information. As a result, the information must be protected from unauthorized access or exploitation. Therefore, organizations must ensure that their systems are transparent to build user confidence. This paper introduces the architectural design and development of a blockchain-based system for private data management, discusses the proof-of-concept prototype using Hyperledger Fabric, and presents evaluation results of the proposed system using Hyperledger Caliper. The proposed solution can be used in any application domain where managing the privacy of user data is important, such as in health care systems.

Tensor Network-Encrypted Physical Anti-counterfeiting Passport for Digital Twin Authentication.

The trend of digitalization has produced rapidly increasing data interaction and authentication demand in today's internet of things ecosystem. To face the challenge, we demonstrated a micro-scale label by direct laser writing to perform as a passport between the physical and digital worlds. On this label, the user information is encrypted into three-dimensional geometric structures by a tensor network and then authenticated through the decryption system based on computer vision. A two-step printing methodology is applied to code the randomly distributed fluorescence from doped quantum dots, which achieved physical unclonable functions (PUFs) of the passport. The 105 bits/mm2 data storage density enables abundant encrypted information from physical worlds, for example, the biometric data of human users. This passport guarantees the strong correlation between the user's privacy data and the PUF-assisted codes, successfully overcoming the illegal transfer of authentication information. Due to its ultra-high security level and convenience, the printed passport has enormous potential in future digital twin authentication anytime anywhere, including personal identity, valuable certificates, and car networking.

Is Homomorphic Encryption-Based Deep Learning Secure Enough?

As the amount of data collected and analyzed by machine learning technology increases, data that can identify individuals is also being collected in large quantities. In particular, as deep learning technology—which requires a large amount of analysis data—is activated in various service fields, the possibility of exposing sensitive information of users increases, and the user privacy problem is growing more than ever. As a solution to this user’s data privacy problem, homomorphic encryption technology, which is an encryption technology that supports arithmetic operations using encrypted data, has been applied to various field including finance and health care in recent years. If so, is it possible to use the deep learning service while preserving the data privacy of users by using the data to which homomorphic encryption is applied? In this paper, we propose three attack methods to infringe user’s data privacy by exploiting possible security vulnerabilities in the process of using homomorphic encryption-based deep learning services for the first time. To specify and verify the feasibility of exploiting possible security vulnerabilities, we propose three attacks: (1) an adversarial attack exploiting communication link between client and trusted party; (2) a reconstruction attack using the paired input and output data; and (3) a membership inference attack by malicious insider. In addition, we describe real-world exploit scenarios for financial and medical services. From the experimental evaluation results, we show that the adversarial example and reconstruction attacks are a practical threat to homomorphic encryption-based deep learning models. The adversarial attack decreased average classification accuracy from 0.927 to 0.043, and the reconstruction attack showed average reclassification accuracy of 0.888, respectively.