Sensitive User Data Research Articles

China's Digital Colonialism: Espionage and Repression Along the Digital Silk Road

Through the Digital Silk Road Initiative (DSR), announced in 2015, the People's Republic of China (PRC) is building expansive global data infrastructure and exporting surveillance technologies to dictators and illiberal regimes throughout the developing world.1 In some cases, these exports involve the trade of technology for access to sensitive user data and facial recognition intelligence. Domestically, China uses this type of technology to assert authority over its citizens, censor the media, quell protests, and systematically oppress religious minorities. Now, over eighty countries are enabled to do the same with Chinese surveillance technology.2 This report explores three primary concerns related to China's digital colonialism. China's DSR projects in the least developed regions of the world create a power imbalance between China and the recipient nations, resulting in a high risk for privacy and cybersecurity in those regions. China's export of intrusive artificial intelligence-enabled technologies and ideologies to illiberal regimes around the world enables authoritarianism and systemic oppression and degrades democratic values. Chinese digital dominance poses both a critical cybersecurity threat to the world and a growing threat to competitors' markets through the assertion of new Chinese-style standards of internet governance. As China builds new global internet infrastructure through the Digital Silk Road, it will co-opt billions of new Internet of Things (IoT) devices, servers, and foreign resources to its cyber arsenal. The author's firm, Recorded Future, posits that Beijing will conduct extensive influence operations in conjunction with the development of Chinese-style internet governance in participating Belt and Road Initiative (BRI) countries. If left unchecked by the rest of the world, China will reshape internet governance by replacing democratic values and standards with authoritarian principles.

Security Vulnerabilities in Microprocessors

Microprocessors such as those found in PCs and smartphones are complex in their design and nature. In recent years, an increasing number of security vulnerabilities have been found within these microprocessors that can leak sensitive user data and information. This report will investigate microarchitecture vulnerabilities focusing on the Spectre and Meltdown exploits and will look at what they do, how they do it and, the real-world impact these vulnerabilities can cause. Additionally, there will be an introduction to the basic concepts of how several PC components operate to support this.

SensitiveNets: Learning Agnostic Representations with Application to Face Images.

This work proposes a novel privacy-preserving neural network feature representation to suppress the sensitive information of a learned space while maintaining the utility of the data. The new international regulation for personal data protection forces data controllers to guarantee privacy and avoid discriminative hazards while managing sensitive data of users. In our approach, privacy and discrimination are related to each other. Instead of existing approaches aimed directly at fairness improvement, the proposed feature representation enforces the privacy of selected attributes. This way fairness is not the objective, but the result of a privacy-preserving learning method. This approach guarantees that sensitive information cannot be exploited by any agent who process the output of the model, ensuring both privacy and equality of opportunity. Our method is based on an adversarial regularizer that introduces a sensitive information removal function in the learning objective. The method is evaluated on three different primary tasks (identity, attractiveness, and smiling) and three publicly available benchmarks. In addition, we present a new face annotation dataset with balanced distribution between genders and ethnic origins. The experiments demonstrate that it is possible to improve the privacy and equality of opportunity while retaining competitive performance independently of the task.

Modelling and Experimental Assessment of Inter-Personal Distancing Based on Shared GNSS Observables.

In the last few years, all countries worldwide have fought the spread of SARS-CoV-2 (COVID-19) by exploiting Information and Communication Technologies (ICT) to perform contact tracing. In parallel, the pandemic has highlighted the relevance of mobility and social distancing among citizens. The monitoring of such aspects appeared prominent for reactive decision-making and the effective tracking of the infection chain. In parallel to the proximity sensing among people, indeed, the concept of social distancing has captured the attention to signal processing algorithms enabling short-to-medium range distance estimation to provide behavioral models in the emergency. By exploiting the availability of smart devices, the synergy between mobile network connectivity and Global Navigation Satellite Systems (GNSS), cooperative ranging approaches allow computing inter-personal distance measurements in outdoor environments through the exchange of light-weight navigation data among interconnected users. In this paper, a model for Inter-Agent Ranging (IAR) is provided and experimentally assessed to offer a naive collaborative distancing technique that leverages these features. Although the technique provides distance information, it does not imply the disclosure of the user’s locations being intrinsically prone to protect sensitive user data. A statistical error model is presented and validated through synthetic simulations and real, on-field experiments to support implementation in GNSS-equipped mobile devices. Accuracy and precision of IAR measurements are compared to other consolidated GNSS-based techniques showing comparable performance at lower complexity and computational effort.

Blockchain-Based Secure Key Management for Mobile Edge Computing

Mobile edge computing (MEC) is a promising edge technology to provide high bandwidth and low latency shared services and resources to mobile users. However, the MEC infrastructure raises major security concerns when the shared resources involve sensitive and private data of users. This paper proposes a novel blockchain-based key management scheme for MEC that is essential for ensuring secure group communication among the mobile devices as they dynamically move from one subnetwork to another. In the proposed scheme, when a mobile device joins a subnetwork, it first generates lightweight key pairs for digital signature and communication, and broadcasts its public key to neighbouring peer users in the subnetwork blockchain. The blockchain miner in the subnetwork packs all the public key of mobile devices into a block that will be sent to other users in the subnetwork. This enables the mobile device to communicate with its peers in the subnetwork by encrypting the data with the public key stored in the blockchain. When the mobile device moves to another subnetwork in the tree network, all the mobile devices of the new subnetwork can quickly verify its identity by checking its record in the local or higher hierarchy subnetwork blockchain. Furthermore, when the mobile device leaves the subnetwork, it does not need to do anything and its records will remain in the blockchain which is an append-only database. Theoretical security analysis shows that the proposed scheme can defend against the 51 percent attack and malicious entities in the blockchain network utilizing Proof-of-Work consensus mechanism. Moreover, the backward and forward secrecy is also preserved. Experimental results demonstrate that the proposed scheme outperforms two baselines in terms of computation, communication and storage.

Towards link inference attack against network structure perturbation

The increasing popularity and diversity of social media sites have resulted in an emergent number of available social networks. These social networks are now the source of information for third-party consumers, such as researchers and advertisers, to understand user social activities. In a privacy-preserving viewpoint, a full assessment of social relationships between individuals may violate privacy. Different network structure perturbation methods have been proposed to limit the disclosure of sensitive user data. However, despite the proliferation of these methods, currently, there are no robustness studies on the methods for link prediction-based hidden inference structure. In this study, we survey the state-of-the-art network structure perturbation methods for privacy-preservation and the classic link prediction methods for structure inference. To restore the perturbed network structure effectively, we propose a novel Multi-Layer Linear Coding-based link prediction method (MLLC) with a closed-form solution. Furthermore, we provide vulnerability analysis on network structure perturbation methods in the context of link prediction-based structure inference. We also compare the methods on the preservation of utility metrics for social network analysis, where a structure perturbation method is preferred if the metrics of the perturbed network are similar to those of the original network. Our experimental study indicates that the MLLC algorithm outperforms conventional methods for hidden structure inference, and that it is important to provide robustness to network structure perturbation methods against these attacks.

Manipulation Attacks in Local Differential Privacy

Local differential privacy is a widely studied restriction on distributed algorithms that collect aggregates about sensitive user data, and is now deployed in several large systems. We initiate a systematic study of a fundamental limitation of locally differentially private protocols: they are highly vulnerable to adversarial manipulation. While any algorithm can be manipulated by adversaries who lie about their inputs, we show that any noninteractive locally differentially private protocol can be manipulated to a much greater extent---when the privacy level is high, or the domain size is large, a small fraction of users in the protocol can completely obscure the distribution of the honest users' input. We also construct protocols that are optimally robust to manipulation for a variety of common tasks in local differential privacy. Finally, we give simple experiments validating our theoretical results, and demonstrating that protocols that are optimal without manipulation can have dramatically different levels of robustness to manipulation. Our results suggest caution when deploying local differential privacy and reinforce the importance of efficient cryptographic techniques for the distributed emulation of centrally differentially private mechanisms.

Resource-Aware Dynamic Service Deployment for Local IoT Edge Computing: Healthcare Use Case

Edge Computing is a novel computing paradigm moving server resources closer to end-devices. In the context of IoT, Edge Computing is a centric technology for enabling reliable, context-aware and low-latency services for several application areas such as smart healthcare, smart industry and smart cities. In our previous work, we have proposed a three-tier IoT Edge architecture and a virtual decentralized service platform based on lightweight microservices, called nanoservices, running on it. Together, these proposals form a basis for virtualizing the available local computational capacity and utilizing it to provide localized resource-efficient IoT services based on the applications’ need. Furthermore, locally-deployed functions are resilient to access network problems and can limit the propagation of sensitive user data for improved privacy. In this paper, we propose an automatic service and resource discovery mechanism for efficient on-the-fly deployment of nanoservices on local IoT nodes. As use case, we have selected a healthcare remote monitoring scenario, which requires high service reliability and availability in a highly dynamic environment. Based on the selected use case, we propose a real-world prototype implementation of the proposed mechanism on Raspberry Pi platform. We evaluate the performance and resource-efficiency of the proposed resource matching function with two alternative deployment approaches: containerized and non-containerized deployment. The results show that the containerized deployment is more resource-efficient, while the resource discovery and matching process takes approximately 6–17 seconds, where containerization adds only 1–1.5 seconds. This can be considered a feasible price for streamlined service management, scalability, resource-efficiency and fault-tolerance.

Sleep apps: current limitations and challenges.

Sleep app ownership is increasing exponentially, due to their accessibility and ease-of-use. However, there are several concerns regarding the use of sleep apps. Few sleep apps demonstrate empirical evidence to support their claims, and if they do, this evidence can be based on significant methodological limitations. In addition, there are data privacy concerns with regards to sleep apps, which share sensitive user data with business and marketing partners, unbeknownst to their users. Moreover, sleep apps may increase engagement with healthcare professionals, which may place additional strain on under-pressure sleep services. This would be compounded by the fact that some sleep apps produce many false positives, and clinicians would need more time to analyze the data provided by these apps. In the future, sleep apps must undergo rigorous validation studies and grant more autonomy to their users over how their data is shared.

Determining the Image Base of Smart Device Firmware for Security Analysis

The authorization mechanism of smart devices is mainly implemented by firmware, yet many smart devices have security issues about their firmware. Limited research has focused on securing the firmware of smart devices, although increasingly more smart devices are used to deal with the very sensitive applications, activities, and data of users. Thus, research on smart device firmware security is of growing importance. Disassembly is a common method for evaluating the security of authorization mechanisms. When disassembling firmware, the processor type of the running environment and the image base of the firmware should first be determined. In general, the processor type can be obtained by tearing down the device or consulting the product manual. However, it is not easy to determine the image base of firmware. Since the processors of many smart devices are ARM architectures, in this paper, we focus on firmware under the ARM architecture and propose an automated method for determining the image base. By studying the storage law of the jump table in the firmware of ARM-based smart devices, we propose an algorithm, named determining the image base by searching jump tables (DBJT), to determine the image base. The experimental results indicate that the proposed method can successfully determine the image base of firmware, which stores the absolute address in the jump table.

Trust in open data applications through transparency

Open data provide great potential for society, for example, in the field of smart cities, from which all citizens might profit. The trust of these citizens is important for the integration of various data, like sensitive user data, into an open data ecosystem. In the following study, we analyzed whether transparency about the application of open data promotes trust. Furthermore, we formulated guidelines on how to create transparency regarding open data in an ethical way. Using an open-data-based fictitious smart city app, we conducted an experiment analyzing to what extent communication of the technical open data application process and the ethical self-commitment for the transparent communication of data application affect trust in the app’s provider. The results indicate that the more information users obtain regarding the use of open data, the more trustworthy they perceive the app provider to be, and the more likely they are to use the app.

Distributed machine learning for energy trading in electric distribution system of the future

Machine Learning (ML) has seen a great potential to solve many power system problems along with its transition into Smart Grid. Specifically, electric distribution systems have witnessed a rapid integration of distributed energy resources (DERs), including photovoltaic (PV) panels, electric vehicles (EV), and smart appliances, etc. Electricity consumers, equipped with such DERs and advanced metering/sensing/computing devices, are becoming self-interested prosumers who can behave more actively for their electric energy consumption. In this paper, the potential of distributed ML in solving the energy trading problem among prosumers of a future electric distribution system - building DC grid cell, is explored, while considering the limited computation, communication, and data privacy issues of the edge entities. A fully distributed energy trading framework based on ML is proposed to optimize the load and price prediction accuracy and energy trading efficiency. Computation resource allocation, communication schemes, ML task scheduling, as well as user sensitive data preserving issues in the distributed ML framework are addressed with consideration of all the economic and physical constraints of the electric distribution systems.

Research gaps based virtualization in mobile cloud computing

Recently, mobile computing is known as a fast-growing utilization of people's daily life. However, the main is the limited mobile devices’ resources such as processing capability, storage space and battery life. With the development of cloud computing, mobile devices’ resources are improved with the help of cloud services, which resulted an emerged technology named Mobile Cloud Computing (MCC). Although the MCC has several advantages for mobile users, it is also challenged by many critical issues like security and privacy of the mobile user's data that offloaded on the cloud’ servers and processed on the virtual machines (VMs). In virtualization, various investigations showed that malicious users are able to break down the cloud security methods by spreading their VMs in order to alter or violate the user sensitive data that executed on cloud’ VMs. This paper deeply analyzes the recent MCC based virtualization approaches and methods by criticizing them. We found out that no approach protects the data from being stolen while distributed VMs that deployed on different cloud servers exchanging data. Hence, the paper provides practical gaps related to virtualization in MCC and future perspectives.

A systematic review of security threats and countermeasures in SaaS

Among the service models provided by the cloud, the software as a service (SaaS) model has had the greatest growth. This service model is an attractive option for organizations, as they can transfer part or all of their IT functions to a cloud service provider. However, there is still some uncertainty about deciding to carry out a migration of all data to the cloud, mainly due to security concerns. The SaaS model not only inherits the security problems of a traditional application, but there are unique attacks and vulnerabilities for a SaaS architecture. Additionally, some of the attacks in this environment are more devastating due to nature of shared resources in the SaaS model. Some of these attacks and vulnerabilities are not yet well known to software designers and developers. This lack of knowledge has negative consequences as it can expose sensitive data of users and organizations. This paper presents a rigorous systematic review using the SALSA framework to know the threats, attacks and countermeasures to mitigate the security problems that occur in a SaaS environment. As part of the results of this review, a classification of threats, attacks and countermeasures in the SaaS environment is presented.

Anonymous Message Authentication Scheme for Semitrusted Edge-Enabled IIoT

As internet of things and other technologies are widely used in industrial manufacturing, automation and intelligence have witnessed rapid developments, resulting in the proposal of the industrial internet of things (IIoT). However, the IIoT still faces various network security threats; hence, data integrity, confidentiality, and anonymity need to be ensured. The use of cloud and edge servers as semitrusted third parties often results in the leaking of privacy sensitive user data. Meanwhile, existing security schemes treat the cloud and edge as fully trusted entities, which is not always valid. Considering edge servers as semitrusted entities, we propose a novel message authentication scheme that leverages group signature technology and proxy reencryption technology to ensure data integrity, confidentiality, and anonymity. Through theoretical analysis and performance comparison, we prove the security of our scheme. In addition, we implement our scheme on a real publish/subscribe system, and the experimental results show the feasibility of our scheme.

Out of Control - Privacy Calculus and the Effect of Perceived Control and Moral Considerations on the Usage of IoT Healthcare Devices.

People are increasingly applying Internet of Things (IoT) devices that help them improve their fitness and provide information about their state of health. Although the acceptance of healthcare devices is increasing throughout the general population, IoT gadgets are reliant on sensitive user data in order to provide full functioning and customized operation. More than in other areas of IoT, healthcare applications pose a challenge to individual privacy. In this study, we examine whether actual and perceived control of collected data affects the willingness to use an IoT healthcare device. We further measure actual behavior as a result of a risk-benefit trade-off within the framework of privacy calculus theory. Our experiment with N = 209 participants demonstrates that while actual control does not affect the willingness to use IoT in healthcare, people have a higher intention to use an IoT healthcare device when they perceive to be in control of their data. Furthermore, we found that, prior to their decision, individuals weigh perceived risks and anticipated benefits of information disclosure, which demonstrates the potential to apply the privacy calculus in the context of IoT healthcare technology. Finally, users’ moral considerations of IoT in healthcare are discussed.

Security Implications for Json web Token Used in MERN Stack for Developing E Commerce Web Application

In almost every organization where user sensitive data is available, security and privacy of the data plays a vital role. As storage of these information is overhead in database, Tokens are generated which handles sessions and also self contains user details. One of such widely used stateless token is Json Web Token. This paper deals with the research that follows implementation of authentication and authorization technique using JSON web token which will make web service a role based one .In the project under taken, Json web token is generated in a more secured way by choosing the secret key for web token wisely. Usually key for the token was a mere string or the set of keys stored in a key ring in the database and used alternately for the users to create the token. Or one more trial model is created where captcha was used in short a random number was generated and used as secret key for token generation but the main issue was increased storage. Thus storage is tried to reduce also less predictive secret key is generated in this project.

Smart Light-Based Information Leakage Attacks

Modern Internet-enabled smart lights promise energy efficiency and many additional capabilities over traditional bulbs. However, these connected lights also expose a new attack surface, which can be maliciously used to violate users' privacy and security. We design and evaluate novel inference attacks that take advantage of the light emitted by these smart lights to infer sensitive user data and preferences.

Analyzing Various Techniques to Safeguard user Sensitive Data

Analyzing Various Techniques to Safeguard user Sensitive Data

What’s really ‘Happning’? A forensic analysis of Android and iOS Happn dating apps

With today’s world revolving around online interaction, dating applications (apps) are a prime example of how people are able to discover and converse with others that may share similar interests or lifestyles, including during the recent COVID-19 lockdowns. To connect the users, geolocation is often utilized. However, with each new app comes the possibility of criminal exploitation. For example, while apps with geolocation feature are intended for users to provide personal information that drive their search to meet someone, that same information can be used by hackers or forensic analysts to gain access to personal data, albeit for different purposes. This paper examines the Happn dating app (versions 9.6.2, 9.7, and 9.8 for iOS devices, and versions 3.0.22 and 24.18.0 for Android devices), which geographically works differently compared to most notable dating apps by providing users with profiles of other users that might have passed by them or in the general radius of their location. Encompassing both iOS and Android devices along with eight varying user profiles with diverse backgrounds, this study aims to explore the potential for a malicious actor to uncover the personal information of another user by identifying artifacts that may pertain to sensitive user data.