Privacy Problems Research Articles

Implementing a Hand Gesture Recognition System Based on Range-Doppler Map.

There have been several studies of hand gesture recognition for human–machine interfaces. In the early work, most solutions were vision-based and usually had privacy problems that make them unusable in some scenarios. To address the privacy issues, more and more research on non-vision-based hand gesture recognition techniques has been proposed. This paper proposes a dynamic hand gesture system based on 60 GHz FMCW radar that can be used for contactless device control. In this paper, we receive the radar signals of hand gestures and transform them into human-understandable domains such as range, velocity, and angle. With these signatures, we can customize our system to different scenarios. We proposed an end-to-end training deep learning model (neural network and long short-term memory), that extracts the transformed radar signals into features and classifies the extracted features into hand gesture labels. In our training data collecting effort, a camera is used only to support labeling hand gesture data. The accuracy of our model can reach 98%.

Mechanisms for Hiding Sensitive Genotypes with Information-Theoretic Privacy.

Motivated by the growing availability of personal genomics services, we study an information-theoretic privacy problem that arises when sharing genomic data: a user wants to share his or her genome sequence while keeping the genotypes at certain positions hidden, which could otherwise reveal critical health-related information. A straightforward solution of erasing (masking) the chosen genotypes does not ensure privacy, because the correlation between nearby positions can leak the masked genotypes. We introduce an erasure-based privacy mechanism with perfect information-theoretic privacy, whereby the released sequence is statistically independent of the sensitive genotypes. Our mechanism can be interpreted as a locally-optimal greedy algorithm for a given processing order of sequence positions, where utility is measured by the number of positions released without erasure. We show that finding an optimal order is NP-hard in general and provide an upper bound on the optimal utility. For sequences from hidden Markov models, a standard modeling approach in genetics, we propose an efficient algorithmic implementation of our mechanism with complexity polynomial in sequence length. Moreover, we illustrate the robustness of the mechanism by bounding the privacy leakage from erroneous prior distributions. Our work is a step towards more rigorous control of privacy in genomic data sharing.

Analysis of the Unexplored Security Issues Common to All Types of NoSQL Databases

NoSQL databases outperform the traditional RDBMS due to their faster retrieval of large volumes of data, scalability, and high performance. The need for these databases has been increasing in recent years because data collection is growing tremendously. Structured, unstructured, and semi- structured data storage is allowed in NoSQL, which is not possible in a traditional database. NoSQL needs to compensate with its security feature for its amazing functionalities of faster data access and large data storage. The main concern exists in sensitive information stored in the data. The need to protect this sensitive data is crucial for confidentiality and privacy problems. To understand the severity of preserving sensitive data, recognizing the security issues is important. These security issues, if not resolved, will cause data loss, unauthorized access, database crashes by hackers, and security breaches. This paper investigates the security issues common to the top twenty NoSQL databases of the following types: document, key-value, column, graph, object- oriented, and multi-model. The top twenty NoSQL databases studied were MongoDB, Cassandra, CouchDB, Hypertable, Redis, Riak, Neo4j, Hadoop HBase, Couchbase, MemcacheDB, RavenDB, Voldemort, Perst, HyperGraphDB, NeoDatis, MyOODB, OrientDB, Apache Drill, Amazon, and Neptune. The comparison results show that there are common security issues among the databases. SQL injection security issues were detected in eight databases. The names of the databases were MongoDB, Cassandra, CouchDB, Neo4j, Couchbase, RavenDB, OrientDB, and Apache Drill.

Digital Forensic Analysis to Improve User Privacy on Android.

The Android platform accounts for 85% of the global smartphone operating-system market share, and recently, it has also been installed on Internet-of-Things (IoT) devices such as wearable devices and vehicles. These Android-based devices store various personal information such as user IDs, addresses, and payment information and device usage data when providing convenient functions to users. Insufficient security for the management and deletion of data stored in the device can lead to various cyber security threats such as personal information leakage and identity theft. Therefore, research on the protection of personal information stored in the device is very important. However, there is a limitation that the current research for protection of personal information on the existing Android platform was only conducted on Android platform 6 or lower. In this paper, we analyze the deleted data remaining on the device and the possibility of recovery to improve user privacy for smartphones using Android platforms 9 and 10. The deleted data analysis is performed based on three data deletion scenarios: data deletion using the app’s own function, data deletion using the system app’s data and cache deletion function, and uninstallation of installed apps. It demonstrates the potential user privacy problems that can occur when using Android platforms 9 and 10 due to the leakage of recovered data. It also highlights the need for improving the security of personal user information by erasing the traces of deleted data that remain in the journal area and directory entry area of the filesystem used in Android platforms 9 and 10.

Compound Keyword Level Search to conserve Privacy in access of Encrypted Cloud

With the advent of cloud computing, data owners are motivated to outsource their complex data management systems from local sites to the commercial public cloud for great flexibility and economic savings. But for protecting data privacy, sensitive data has to be encrypted before outsourcing, which obsoletes traditional data utilization based on plaintext keyword search. Thus, enabling an encrypted cloud data search service is of paramount importance. Considering the large number of data users and documents in the cloud, it is necessary to allow multiple keywords in the search request and return documents in the order of their relevance to these keywords. Related works on searchable encryption focus on single keyword search or Boolean keyword search, and rarely sort the search results. In this paper, for the first time, we define and solve the challenging problem of privacy preserving multi-keyword ranked search over encrypted cloud data (MRSE).We establish a set of strict privacy requirements for such a secure cloud data utilization system. Among various multikeyword semantics, we choose the efficient similarity measure of “coordinate matching”, i.e., as many matches as possible, to capture the relevance of data documents to the search query. We further use “inner product similarity” to quantitatively evaluate such similarity measure.

Privacy-Preserving Techniques in Social Distancing Applications: A Comprehensive Survey

During the world’s challenge to confront the rapidly spreading coronavirus disease (COVID-19) pandemic and the consequent heavy losses and disruption to society, returning to normal life has become a demand. Social distancing, also known as physical distancing, plays a pivotal role in this scenario. Social distancing is a practice to maintain a safe space between a person and others who are not from the same household, preventing the spread of contagious viral diseases. To support this case, several public authorities and governments around the world have proposed social distancing applications (also known as contact-tracing apps). However, the adoption of these applications is arguable because of concerns regarding privacy and user data protection. In this study, we present a comprehensive survey of privacy-preserving techniques for social distancing applications. We provide an extensive background on social distancing applications, including measuring the physical distance between people. We also discuss various privacy-preserving techniques that are used by social distancing applications; specifically, we thoroughly analyze and compare these applications, considering multiple features. Finally, we provide insights and recommendations for designing social distancing applications while reducing the burden of privacy problems.

Privacy Protection Technology Based on Machine Learning and Intelligent Data Recognition

The machine learning algorithm is gradually being applied to various fields and has become the core technology to achieve artificial intelligence. The success of machine learning cannot be achieved without the support of large amounts of data and computing power, which are usually collected through crowdsourcing and learned online. The data collected for machine learning training often contains some personal and sensitive information, including personal mobile phone numbers, ID numbers, and medical information. How to protect these private data at low cost and efficiently is an important problem. Aiming at this kind of problem, this article starts with the privacy problem in machine learning and the way of being attacked and summarizes the privacy protection methods and characteristics in the machine learning algorithm. Then, for the classification accuracy of the different algorithm that uses noise to protect privacy, a deep difference privacy protection method combined with a convolutional neural network is proposed. This method perfectly integrates the features of difference and Gaussian distribution and can obtain the privacy budget of each layer of the neural network. Finally, the stochastic gradient descent algorithm's gradient value is employed to set the Gaussian noise scale and preserve the data's sensitive information. The experimental results demonstrated that by adjusting the parameters of the depth differential privacy model based on differences in private information in the data, a balance between the availability and privacy protection of the training data set could be reached.

BC-EdgeFL: A Defensive Transmission Model Based on Blockchain-Assisted Reinforced Federated Learning in IIoT Environment

Under the times of the Industrial Internet of Things, the traditional centralized machine learning management method cannot deal with such huge data streams, and the problem of data privacy has aroused widespread concern. In view of these difficulties, in this article, we use the advantages of edge computing and federated learning, combined with the outstanding characteristics of the blockchain, to propose a secure data transmission method. First, we separate the local model updating process from the mobile device independent process; second, we add an edge server so that most of the computation is carried out on the server, which improves the learning efficiency; and finally, we use a distributed architecture of the blockchain to protect data security and privacy. Extensive simulation experiments show that the accuracy of our model can reach 98 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\%$</tex-math></inline-formula> . In addition, BC-EdgeFLs interception rate of illegal information can reach 0.8, which has good defensive capabilities. Therefore, the security of data transmission can be strongly guaranteed.

Certificate Management Scheme for VANETs Using Blockchain Structure

Vehicular Ad-hoc NETworks (VANETs), a special kind of Mobile Ad-hoc NETworks (MANETs), play an important role in Intelligent Transportation Systems (ITS). Via wireless technology, vehicles exchange information related to road conditions and their status, and, thereby, VANETs enhance transportation safety and efficiency. A critical aspect of VANETs is providing privacy for the vehicles. The employment of pseudonym certificates is a well-known solution to the privacy problems in VANETs. However, certificate management faces challenges in renewing certificates and revoking vehicles. The centralized certificate management, especially resulting in the delay of the revocation process, harms the nodes of VANETs. This paper proposes a blockchain structure-based certificate management for VANETs and voting-based revocation to halt misbehaving vehicles’ actions. Moreover, this paper presents extended privacy for the participants of the voting process using ring signatures.

Digital Copyright Protection: Focus on Some Relevant Solutions

Copyright protection of digital content is considered a relevant problem of the current Internet since content digitalization and high performance interconnection networks have greatly increased the possibilities to reproduce and distribute digital content. Digital Rights Management (DRM) systems try to prevent the inappropriate or illegal use of copyrighted digital content. They are promoted by the major global media players, but they are also perceived as proprietary solutions that give rise to classic problems of privacy and fair use. On the other hand, watermarking protocols have become a possible solution to the problem of copyright protection. They have evolved during the last decade, and interesting proposals have been designed. This paper first presents current trends concerning the most significant solutions to the problem of copyright protection based on DRM systems and then focuses on the most promising approaches in the field of watermarking protocols. In this regard, the examined protocols are discussed in order to individuate which of them can better represent the right trade-off between opposite goals, such as, for example, security and easy of use, so as to prove that it is possible to implement open solutions compatible with the current web context without resorting to proprietary architectures or impairing the protection of copyrighted digital content.

A conceptualization of the privacy concerns of cloud users

PurposeThe purpose of this paper is to improve the understanding of cloud service users’ privacy concerns, which are anticipated to considerably hinder cloud service market growth. The researchers have explored privacy concerns from dimensions that were identified as relevant in the cloud context.Design/methodology/approachContent analysis was used to identify privacy problems that were most often raised in previous cloud research. Multidimensional developmental theory (MDT) was used to build a conceptual model of cloud privacy concerns. Literature review was made to identify the privacy-related constructs used to measure privacy concerns in previous cloud research.FindingsThe paper provides systematization of recent cloud privacy research, proposal of a conceptual model of cloud privacy concerns, identification of measuring instruments that were used to measure privacy concerns in previous cloud research and identification of categories of problems that need to be addressed in future cloud research.Originality/valueThis paper has identified the categories of privacy problems and dimensions that have not yet been measured in the cloud context, to the best of the authors’ knowledge. Their simultaneous examination could clarify the effects of different dimensions on the privacy concerns of cloud users. The conceptual model of cloud privacy concerns will allow cloud service providers to focus on key cloud problems affecting users’ privacy concerns and use the most appropriate privacy protection communication and preservation approaches.

Task offloading mechanism based on federated reinforcement learning in mobile edge computing

With the arrival of 5G, latency-sensitive applications are becoming increasingly diverse. Mobile Edge Computing (MEC) technology has the characteristics of high bandwidth, low latency and low energy consumption, and has attracted much attention among researchers. To improve the Quality of Service (QoS), this study focuses on computation offloading in MEC. We consider the QoS from the perspective of computational cost, dimensional disaster, user privacy and catastrophic forgetting of new users. The QoS model is established based on the delay and energy consumption and is based on DDQN and a Federated Learning (FL) adaptive task offloading algorithm in MEC. The proposed algorithm combines the QoS model and deep reinforcement learning algorithm to obtain an optimal offloading policy according to the local link and node state information in the channel coherence time to address the problem of time-varying transmission channels and reduce the computing energy consumption and task processing delay. To solve the problems of privacy and catastrophic forgetting, we use FL to make distributed use of multiple users’ data to obtain the decision model, protect data privacy and improve the model universality. In the process of FL iteration, the communication delay of individual devices is too large, which affects the overall delay cost. Therefore, we adopt a communication delay optimization algorithm based on the unary outlier detection mechanism to reduce the communication delay of FL. The simulation results indicate that compared with existing schemes, the proposed method significantly reduces the computation cost on a device and improves the QoS when handling complex tasks.

Protecting user privacy in location-based services over road networks

ABSTRACT In location-based services (LBS), the users with mobile devices share information such as their locations and queries to obtain desired services. Additionally, LBS providers are often able to track or record the movements of their users with great spatial and temporal accuracy. However, user location information can be misused by LBS providers or attackers, which creates privacy problems for users. This raises concerns about privacy breaches; therefore, many approaches have been proposed to protect user privacy. Thus, in previous works, the authors have proposed approaches to maintain the privacy of mobile users in the Euclidean space. However, it is difficult to apply current methods of Euclidean space to the environment of the road networks due to the topological characteristics of the road networks. In this paper, we propose a new approach to preserve user privacy in LBS over road networks. Additionally, in this paper, we provide new methods for selecting segments and users on the road network to face the attacks. Moreover, to evaluate our work, we compared it with other works such as MCC, BATCH, V-DCA, D-TC, and GCA approaches. After comparing these works, our work offers a good balance between quality of service and privacy.

Privacy-Preserving Physical-Layer Authentication for Non-Orthogonal Multiple Access Systems

This paper addresses the problems of both authentication and privacy in a Non-Orthogonal Multiple Access (NOMA) system by using a privacy-preserving Physical-Layer Authentication (PLA) scheme. The prior scheme has the following limitations: no privacy, low authentication performance, management problem of different secret keys, and constraint to the two-user scenario. In this paper, we propose two privacy-preserving PLA schemes: the Privacy-Preserving PLA (PP-PLA) scheme and the Enhanced Privacy-Preserving PLA (EPP-PLA) scheme, which improve both the privacy and authentication performance as compared to the prior scheme. The fundamental difference between the EPP-PLA and PP-PLA schemes is how to encrypt the source message of a certain user while the other operations of the two proposed schemes keep the same. We provide the theoretical analysis of the proposed schemes in terms of robustness, compatibility, privacy, and security, and derive their closed-form expressions. Moreover, we provide the theoretical comparisons between the proposed schemes and the prior scheme. We optimize the parameters of the proposed schemes to achieve both message-rate fairness and authentication-accuracy fairness. We implement the proposed schemes and conduct extensive performance comparisons through simulations. Experimental results show that the theoretical results perfectly match the corresponding simulation results. Specifically, we take the second user, acting as an administrator for a NOMA system with three users, as an example, where SNR = 16 dB. In comparison with the prior scheme, both proposed schemes have the same compatibility, whereas they improve the robustness by 4.72%. Moreover, the EPP-PLA and PP-PLA schemes improve the security by 32.8%, whereas they improve the privacy by 3000.75% and 1000.75%, respectively.

Problems of adoption privacy ensuring in considering of civil case on adoption (adrogation) of a child in the courts of the Republic of Kazakhstan

Problems of adoption privacy ensuring in considering of civil case on adoption (adrogation) of a child in the courts of the Republic of Kazakhstan

Blockchain-Based Information Supervision Model for Rice Supply Chains.

Rice is a major food crop around the world, and its various quality and safety problems are closely related to human health. As an important area of food safety research, the rice supply chain has attracted increasing attention. Based on blockchain technology, this study investigated problems of data privacy and circulation efficiency caused by complex rice supply networks, long circulation cycles, and various risk factors in each link. First, we deconstructed the quality and safety of each link of the rice supply chain at the information level and established a key information classification table for each link. On that basis, we built a rice supply chain information supervision model based on blockchain. Various encryption algorithms are used to secure the sensitive data of enterprises in the supply chain to meet regulators' needs for efficient supervision. Moreover, we propose a practical Byzantine fault-tolerant consensus algorithm that scores the credit of enterprise nodes, optimizes the selection strategy of master nodes, and ensures high efficiency and low cost. Then, we built a prototype system based on the open-source framework of hyperledger fabric, analyzed the model's viability, and implemented the system using cases. The results indicated that the proposed system can optimize the information supervision process of rice supply chain regulators and provide a feasible solution for the quality and safety supervision of grain and oil.

A federated learning framework for cyberattack detection in vehicular sensor networks

Vehicular Sensor Networks (VSN) introduced a new paradigm for modern transportation systems by improving traffic management and comfort. However, the increasing adoption of smart sensing technologies with the Internet of Things (IoT) made VSN a high-value target for cybercriminals. In recent years, Machine Learning (ML) and Deep Learning (DL) techniques attracted the research community to develop security solutions for IoT networks. Traditional ML and DL approaches that operate with data stored on a centralized server raise major privacy problems for user data. On the other hand, the resource-constrained nature of a smart sensing network demands lightweight security solutions. To address these issues, this article proposes a Federated Learning (FL)-based attack detection framework for VSN. The proposed scheme utilizes a group of Gated Recurrent Units (GRU) with a Random Forest (RF)-based ensembler unit. The effectiveness of the suggested framework is investigated through multiple performance metrics. Experimental findings indicate that the proposed FL approach successfully detected the cyberattacks in VSN with the highest accuracy of 99.52%. The other performance scores, precision, recall, and F1 are attained as 99.77%, 99.54%, and 99.65%, respectively.

Efficient and Privacy Preserving Clustering Algorithm for Spatiotemporal Data

The efficiency of a spatiotemporal data analysis algorithm decreases as the amount of data increases. Many clustering techniques have been proposed for data analysis applications. However, applying those techniques to spatiotemporal data clustering is still in its infancy. In this paper, we tackle the issue of clustering spatiotemporal data on public Cloud based on the distance between them. To increase the efficiency of spatiotemporal clustering, we have proposed a MapReduce-based framework for clustering. However, as spatiotemporal dataset contains sensitive information, directly outsourcing spatiotemporal data to Cloud servers will raise privacy concerns. To address the problem of privacy, we have proposed a privacy preserving clustering algorithm based on MapReduce for spatiotemporal data that can be efficiently outsourced for data processing on the Cloud servers. The proposed scheme allows the clustering operation to be performed directly on the encrypted spatiotemporal data by Cloud server. Extensive experimental evaluation with trajectory data shows that our scheme efficiently produces higher quality clustering results.

The problem of privacy in the processing of personal data in Russia at the present stage

The problem of privacy in the processing of personal data in Russia at the present stage

IMPACT OF SOCIAL MEDIA USE: ANALYSIS OF CHARACTER CHANGE IN ISLAM

Social media is an online medium, with its users being able to easily participate, share, and create content including blogs, social networks, wikis, forums and virtual worlds. Blogs, social networks and wikis are the most common forms of social media used by people around the world. The positive impact of social media is that it makes it easier for us to interact with many people, expand relationships, distance and time are no longer a problem, easier to express ourselves, information dissemination can take place quickly, costs are cheaper. While the negative impact of social media is to keep people who are close and vice versa, face-to-face interactions tend to decrease, making people addicted to the internet, causing conflicts, privacy problems, vulnerable to bad influences from others. The existence of social media has influenced social life in society. Changes in social relationships or as changes to the balance (equilibrium) of social relations and all forms of changes in social institutions in a society, which affect the social system, including values, attitudes and behavior patterns among groups in society. Positive social changes such as the ease of obtaining and conveying information, obtaining social and economic benefits. Meanwhile, social changes tend to be negative, such as the emergence of social groups in the name of religion, ethnicity and certain behavioral patterns that sometimes deviate from existing norms.