Improve User Privacy Research Articles

Comparative analysis of DNS over HTTPS detectors

DNS over HTTPS (DoH) is a protocol that encrypts DNS traffic to improve user privacy and security. However, its use also poses challenges for network operators and security analysts who need to detect and monitor network traffic for security purposes. Therefore, there are multiple DoH detection proposals that leverage machine learning to identify DoH connections; however, these proposals were often tested on different datasets, and their evaluation methodologies were not consistent enough to allow direct performance comparison. In this study, seven DoH detection proposals were recreated and evaluated with six different experiments to answer research questions that targeted specific deployment scenarios concerning ML-model transferability, usability, and longevity. For thorough testing, a large Collection of DoH datasets along with a novel 5-week dataset was used, which enabled the evaluation of models’ longevity. This study provides insights into the current state of DoH detection techniques and evaluates the models in scenarios that have not been previously tested. Therefore, this paper goes beyond classical replication studies and shows previously unknown properties of seven published DoH detectors.

Reduced-Order Neural Network Synthesis With Robustness Guarantees.

In the wake of the explosive growth in smartphones and cyber-physical systems, there has been an accelerating shift in how data are generated away from centralized data toward on-device-generated data. In response, machine learning algorithms are being adapted to run locally on board, potentially hardware-limited, devices to improve user privacy, reduce latency, and be more energy efficient. However, our understanding of how these device-orientated algorithms behave and should be trained is still fairly limited. To address this issue, a method to automatically synthesize reduced-order neural networks (having fewer neurons) approximating the input-output mapping of a larger one is introduced. The reduced-order neural network's weights and biases are generated from a convex semidefinite program that minimizes the worst case approximation error with respect to the larger network. Worst case bounds for this approximation error are obtained and the approach can be applied to a wide variety of neural networks architectures. What differentiates the proposed approach to existing methods for generating small neural networks, e.g., pruning, is the inclusion of the worst case approximation error directly within the training cost function, which should add robustness to out-of-sample data points. Numerical examples highlight the potential of the proposed approach. The overriding goal of this article is to generalize recent results in the robustness analysis of neural networks to a robust synthesis problem for their weights and biases.

Interactive Privacy Management: Toward Enhancing Privacy Awareness and Control in the Internet of Things

The balance between protecting user privacy while providing cost-effective devices that are functional and usable is a key challenge in the burgeoning Internet of Things (IoT). In traditional desktop and mobile contexts, the primary user interface is a screen; however, in IoT devices, screens are rare or very small, invalidating many existing approaches to protecting user privacy. Privacy visualizations are a common approach for assisting users in understanding the privacy implications of web and mobile services. To gain a thorough understanding of IoT privacy, we examine existing web, mobile, and IoT visualization approaches. Following that, we define five major privacy factors in the IoT context: type, usage, storage, retention period, and access. We then describe notification methods used in various contexts as reported in the literature. We aim to highlight key approaches that developers and researchers can use for creating effective IoT privacy notices that improve user privacy management (awareness and control). Using a toolkit, a use case scenario, and two examples from the literature, we demonstrate how privacy visualization approaches can be supported in practice.

Data Instrumentation From IoT Network Traffic as Support for Security Management

The Internet of Things revolutionizes human life by inaugurating scenarios such as smart homes. However, IoT devices contain much sensitive information about users and devices from a security and privacy perspective. Through traffic-based attacks, adversaries map changes in traffic rates to a particular in-home user’s actions. An efficient IoT data instrumentation may protect users against traffic-based attacks by giving valuable information to adaptive network security solutions. Nonetheless, no work performs data instrumentation or uses feature exploration to reveal relevant features to assist network security management. Thus, this article introduces IoTReGuard, an IoT Method to Reveal and Guard IoT Network Traffic Features. IoTReguard aims to explore network traffic features to reveal the most relevant ones and hide them to protect users’ privacy. By IoT network feature exploration and data instrumentation, IoTReGuard provides valuable information on network traffic features to mask critical features. Results showed that IoTReGuard reduced from 70% to 20% of F1-Score on identifying the IoT devices, improving user privacy.

Small scale IoT device privacy evaluation using Petri net modeling

Most Small Scale IoT (SSIoT) devices on the market gather a significant amount of sensitive information, yet many lack privacy controls, introducing significant privacy and safety risk to users. Such risks stem from the lack of privacy integration into the system development process. No formalized SSIoT data flow model currently integrates privacy elements for evaluation during the system development lifecycle (SDLC). This work aims to review current data flow modeling techniques, used in most SSIoT System Development Lifecycle (SDLC), to identify privacy gaps and assess requisite privacy controls necessary to improve user privacy. To verify this, we designed a simulation experiment using Petri net to evaluate the current privacy controls and hotspots during SSIoT data transitions. We assess our Petri net model using a Barbie Smart connected toy user interaction. The results show that Petri net has unique privacy elements and verification schemes over all other data flow modeling techniques. Further, it provides privacy assurance, evaluates privacy by identifying privacy hotspots needing controls, and minimizes privacy-related risks such as breach of personally identifiable information and interaction data during SSIoT device use.

Improving User Privacy in Identity-Based Encryption Environments

The promise of identity-based systems is that they maintain the functionality of public key cryptography while eliminating the need for public key certificates. The first efficient identity-based encryption (IBE) scheme was proposed by Boneh and Franklin in 2001; variations have been proposed by many researchers since then. However, a common drawback is the requirement for a private key generator (PKG) that uses its own master private key to compute private keys for end users. Thus, the PKG can potentially decrypt all ciphertext in the environment (regardless of who the intended recipient is), which can have undesirable privacy implications. This has led to limited adoption and deployment of IBE technology. There have been numerous proposals to address this situation (which are often characterized as methods to reduce trust in the PKG). These typically involve threshold mechanisms or separation-of-duty architectures, but unfortunately often rely on non-collusion assumptions that cannot be guaranteed in real-world settings. This paper proposes a separation architecture that instantiates several intermediate CAs (ICAs), rather than one (as in previous work). We employ digital credentials (containing a specially-designed attribute based on bilinear maps) as the blind tokens issued by the ICAs, which allows a user to easily obtain multiple layers of pseudonymization prior to interacting with the PKG. As a result, our proposed architecture does not rely on unrealistic non-collusion assumptions and allows a user to reduce the probability of a privacy breach to an arbitrarily small value.

Fuzzy logic based authentication in cognitive radio networks

<p>Security is a critical issue in cognitive radio networks because the cognitive node enters and variably leaves the spectrum, so it is difficult to process communication secretly. We suggested a fuzzy logic-based implicit authentication mechanism to be a solution for the confusion if there were any cognitive node doubts it to be unauthentic, and to improve user privacy in cognitive radio networks. Using a fuzzy logic technique, the proposed scheme computed certification based on proposed feedback. When a cognitive node needs to join the network, it is verified by using fuzzy logic if the node was authenticated or not. Our proposed fuzzy logic's results implicit authentication proved that it was a very successful and applicable scheme on cognitive radio networks, and it will be able to make an effective final decision in the context of incompleteness, ambiguity, and heterogeneity</p>

Self-Sovereign Identity: A Systematic Review, Mapping and Taxonomy.

Self-Sovereign Identity (SSI) is an identity model centered on the user. The user maintains and controls their data in this model. When a service provider requests data from the user, the user sends it directly to the service provider, bypassing third-party intermediaries. Thus, SSI reduces identity providers’ involvement in the identification, authentication, and authorization, thereby increasing user privacy. Additionally, users can share portions of their personal information with service providers, significantly improving user privacy. This identity model has drawn the attention of researchers and organizations worldwide, resulting in an increase in both scientific and non-scientific literature on the subject. This study conducts a comprehensive and rigorous systematic review of the literature and a systematic mapping of theoretical and practical advances in SSI. We identified and analyzed evidence from reviewed materials to address four research questions, resulting in a novel SSI taxonomy used to categorize and review publications. Additionally, open challenges are discussed along with recommendations for future work.

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.

A Survey on Location Privacy Attacks and Prevention Deployed with IoT in Vehicular Networks

Vehicular ad hoc networks (VANETs) connect two or more vehicles wirelessly to enable data exchange in an Internet of Things (IoT) environment. In VANETs, location privacy is the most crucial piece of information, and its protection is the top priority. However, the location privacy threats have not been adequately addressed in positioning for IoT in VANETs. This paper provides an overview of location privacy attacks and their solutions to address the problems caused by attacks in any IoT environment. Secondly, we have analyzed specific solutions based on anonymity (pseudonym) and cryptographic solutions using a digital signature technique. This enables to improve user privacy and security of location-based services for IoT in VANET. Moreover, we have proposed a faster 5G solution for the VANETs as it rapidly disseminates the data in fast-moving vehicles.

In Platforms We Trust?Unlocking the Black-Box of News Algorithms through Interpretable AI

ABSTRACT With the rapid increase in the use and implementation of AI in the journalism industry, the ethical issues of algorithmic journalism have grown rapidly and resulted in a large body of research that applied normative principles such as privacy, information disclosure, and data protection. Understanding how users’ information processing leads to information disclosure in platformized news contexts can be important questions to ask. We examine users’ cognitive routes leading to information disclosure by testing the effect of interpretability on privacy in algorithmic journalism. We discuss algorithmic information processing and show how the process can be utilized to improve user privacy and trust.

Privacy enforcement on subscribers data in cloud computing


 
 
 Data stored in the cloud are susceptible to an array of threats from hackers. This is because threats, hackers and unauthorized access are not supported by the cloud service providers as implied. This study improves user privacy in the cloud system, using privacy with non-trusted provider (PNTP) on software and platform as a service model. The subscribers encrypt the data using user’s personal Advanced Encryption Standard (AES) symmetric key algorithm and send the encrypted data to the storage pool of the Cloud Service Provider (CSP) via a secure socket layer. The AES performs a second encryption on the data sent to the cloud and generates for the subscriber a key that will be used for decryption of previously stored data. The encryption and decryption keys are managed by the key server and have been hardcoded into the PNTP system. The model was simulated using the Stanford University multimedia dataset and benchmarked with a Privacy with Trusted cloud Provider (PTP) model using encryption time, decryption time and efficiency (brute force hacking) as parameters. Results showed that it took a longer time to access the user files in PNTP than in the PTP system. The brute force hacking took a longer time (almost double) to access data stored on the PNTP system. This will give subscribers a high level of control over their data and increase the adoption of cloud computing by businesses and organizations with highly sensitive information.
 
 

Locally Decodable Index Codes

An index code for broadcast channel with receiver side information is locally decodable if each receiver can decode its demand by observing only a subset of the transmitted codeword symbols instead of the entire codeword. Local decodability in index coding is known to reduce receiver complexity, improve user privacy and decrease decoding error probability in wireless fading channels. Conventional index coding solutions assume that the receivers observe the entire codeword, and as a result, for these codes the number of codeword symbols queried by a user per decoded message symbol, which we refer to as locality , could be large. In this paper, we pose the index coding problem as that of minimizing the broadcast rate for a given value of locality (or vice versa) and designing codes that achieve the optimal trade-off between locality and rate. We identify the optimal broadcast rate corresponding to the minimum possible value of locality for all single unicast problems. We present new structural properties of index codes which allow us to characterize the optimal trade-off achieved by: vector linear codes when the side information graph is a directed cycle; and scalar linear codes when the minrank of the side information graph is one less than the order of the problem. We also identify the optimal trade-off among all codes, including non-linear codes, when the side information graph is a directed 3-cycle. Finally, we present techniques to design locally decodable index codes for arbitrary single unicast problems and arbitrary values of locality.

Smart media and application

Smart media and application

Efficient dummy generation for considering obstacles and protecting user location

SummaryTo receive location‐based services (LBS), users must disclose their locations and queries to the LBS server, which can expose the user's identity, location, and other information. Recently, techniques for protecting user privacy using dummies have been researched. However, many factors, such as the distance between the obstacles and the dummies, must be considered in order to create dummies. Therefore, this study proposes an efficient dummy creation technique to improve user privacy protection. Experimental results show that the proposed technique improves on other recent techniques.

Device-Based Security to Improve User Privacy in the Internet of Things †.

The use of Internet of Things (IoT) is rapidly growing and a huge amount of data is being generated by IoT devices. Cloud computing is a natural candidate to handle this data since it has enough power and capacity to process, store and control data access. Moreover, this approach brings several benefits to the IoT, such as the aggregation of all IoT data in a common place and the use of cloud services to consume this data and provide useful applications. However, enforcing user privacy when sending sensitive information to the cloud is a challenge. This work presents and evaluates an architecture to provide privacy in the integration of IoT and cloud computing. The proposed architecture, called PROTeCt—Privacy aRquitecture for integratiOn of internet of Things and Cloud computing, improves user privacy by implementing privacy enforcement at the IoT devices instead of at the gateway, as is usually done. Consequently, the proposed approach improves both system security and fault tolerance, since it removes the single point of failure (gateway). The proposed architecture is evaluated through an analytical analysis and simulations with severely constrained devices, where delay and energy consumption are evaluated and compared to other architectures. The obtained results show the practical feasibility of the proposed solutions and demonstrate that the overheads introduced in the IoT devices are worthwhile considering the increased level of privacy and security.

Technical Privacy Metrics

The goal of privacy metrics is to measure the degree of privacy enjoyed by users in a system and the amount of protection offered by privacy-enhancing technologies. In this way, privacy metrics contribute to improving user privacy in the digital world. The diversity and complexity of privacy metrics in the literature make an informed choice of metrics challenging. As a result, instead of using existing metrics, new metrics are proposed frequently, and privacy studies are often incomparable. In this survey, we alleviate these problems by structuring the landscape of privacy metrics. To this end, we explain and discuss a selection of over 80 privacy metrics and introduce categorizations based on the aspect of privacy they measure, their required inputs, and the type of data that needs protection. In addition, we present a method on how to choose privacy metrics based on nine questions that help identify the right privacy metrics for a given scenario, and highlight topics where additional work on privacy metrics is needed. Our survey spans multiple privacy domains and can be understood as a general framework for privacy measurement.

Attribute-centric anonymization scheme for improving user privacy and utility of publishing e-health data

The adoption of advanced technologies in the healthcare sector has brought about many improvements in the industry, including better communication between healthcare providers, improved quality of treatment, and reduced cost. For the most part, these improvements have come about due to collaboration between healthcare providers and the sharing of healthcare data. However, this introduces various security and privacy concerns pertaining to the data in question. Preserving the privacy of the patients while simultaneously sharing data that would facilitate medical research is absolutely essential, for it is not just an ethical requirement but is also dictated by the regulations. In this paper, we propose a new anonymization scheme of data privacy for e-health records which differs from existing approaches in its ability to prevent from identity disclosure even faced with adversaries having pertinent background knowledge. The proposed scheme is based on transforming data into fixed intervals and then replacing original values with averages. As a result, the proposed scheme offers improved data privacy and utility in privacy preserving data publishing. The simulation results show the effectiveness of the scheme and verify the aforementioned claims.

User Trust on Online Social Network on the basis of Security and privacy

Online social networks have become a popular medium of sharing information and also major source for connecting the people on a network. As we know that the threats related to online social network is affecting people to great extent, and is resulting in loss of trust among the people. The trust is basically the measure of confidence that an entity and entities will show in different manners, and privacy is basically a set of actions for controlling personal information. Our basic purpose is to improve user privacy and to build user trust on online social networks. For this purpose, we have conducted survey of users especially teenagers and have taken their views about their knowledge of maintaining privacy on social networks, whether they review privacy settings or leave it as it is, without knowing the importance of privacy. After getting survey result we concluded that most of teenagers are unaware of maintaining privacy so they did not review their privacy settings, resultantly they became a victim of different threats related to online social networks. For this purpose, the tool is designed for reviewing or checking the user’s privacy settings and generating different alerts for achieving desired privacy level.

Vulnerability- and Diversity-Aware Anonymization of Personally Identifiable Information for Improving User Privacy and Utility of Publishing Data.

Personally identifiable information (PII) affects individual privacy because PII combinations may yield unique identifications in published data. User PII such as age, race, gender, and zip code contain private information that may assist an adversary in determining the user to whom such information relates. Each item of user PII reveals identity differently, and some types of PII are highly identity vulnerable. More vulnerable types of PII enable unique identification more easily, and their presence in published data increases privacy risks. Existing privacy models treat all types of PII equally from an identity revelation point of view, and they mainly focus on hiding user PII in a crowd of other users. Ignoring the identity vulnerability of each type of PII during anonymization is not an effective method of protecting user privacy in a fine-grained manner. This paper proposes a new anonymization scheme that considers the identity vulnerability of PII to effectively protect user privacy. Data generalization is performed adaptively based on the identity vulnerability of PII as well as diversity to anonymize data. This adaptive generalization effectively enables anonymous data, which protects user identity and private information disclosures while maximizing the utility of data for performing analyses and building classification models. Additionally, the proposed scheme has low computational overheads. The simulation results show the effectiveness of the scheme and verify the aforementioned claims.