Privacy Demands Research Articles

The Impact of Data Privacy on Users' Smartphone App Adoption Decisions

Rich user information is gained through user tracking and power mobile smartphone applications. Apps thereby become aware of the user and their context, enabling intelligent and adaptive applications. However, such data poses severe privacy risks. Although users are only partially aware of them, awareness increases with the proliferation of privacy-enhancing technologies. How privacy literacy and raising privacy concerns affect app adoption is unclear; however, we hypothesize that it leads to a lower adoption rate of data-heavy smartphone apps, as non-usage often is the user's only option to protect themselves. We conducted a survey (N=100) to investigate the relationship between privacy-relevant app- and publisher characteristics with the users' intention to install and use it. We found that users are especially critical of contentful data types and apps with rights to perform actions on their behalf. On the other hand, the expectation of a productive benefit induced by the app can increase the app-adoption intention. Our findings show which aspects designers of privacy-enhancing technologies should focus on to meet the demand for more user-centered privacy.

An Effective Federated Object Detection Framework with Dynamic Differential Privacy

The proliferation of data across multiple domains necessitates the adoption of machine learning models that respect user privacy and data security, particularly in sensitive scenarios like surveillance and medical imaging. Federated learning (FL) offers a promising solution by decentralizing the learning process, allowing multiple participants to collaboratively train a model without sharing their data. However, when applied to complex tasks such as object detection, standard FL frameworks can fall short in balancing the dual demands of high accuracy and stringent privacy. This paper introduces a sophisticated federated object detection framework that incorporates advanced differential privacy mechanisms to enhance privacy protection. Our framework is designed to work effectively across heterogeneous and potentially large-scale datasets, characteristic of real-world environments. It integrates a novel adaptive differential privacy model that strategically adjusts the noise scale during the training process based on the sensitivity of the features being learned and the progression of the model’s accuracy. We present a detailed methodology that includes a privacy budget management system, which optimally allocates and tracks privacy expenditure throughout training cycles. Additionally, our approach employs a hybrid model aggregation technique that not only ensures robust privacy guarantees but also mitigates the degradation of object detection performance typically associated with DP. The effectiveness of our framework is demonstrated through extensive experiments on multiple benchmark datasets, including COCO and PASCAL VOC. Our results show that our framework not only adheres to strict DP standards but also achieves near-state-of-the-art object detection performance, underscoring its practical applicability. For example, in some settings, our method can lower the privacy success rate by 40% while maintaining high model accuracy. This study makes significant strides in advancing the field of privacy-preserving machine learning, especially in applications where user privacy cannot be compromised. The proposed framework sets a new benchmark for implementing federated learning in complex, privacy-sensitive tasks and opens avenues for future research in secure, decentralized machine learning technologies.

Study and Review of GupShup- A Chat Application

In the digital age, the demand for secure and private messaging platforms has surged, paralleling the exponential growth of online interactions. This research introduces Secure GupShup, a groundbreaking application designed to address the paramount concerns surrounding user privacy and data security. In an environment where information confidentiality is of utmost importance, Secure GupShup stands out by providing a feature-rich experience that includes private and public chat rooms, with a distinctive emphasis on security through the implementation of instant disappearing messages. The core innovation of Secure GupShup lies in its implementation of instant disappearing messages, a feature designed to augment user privacy. By ensuring that messages automatically vanish after a predetermined time, the application adds an extra layer of security, preventing unauthorized access to sensitive information. This not only grants users greater control over their conversations but also fosters an environment where information is transient, mitigating the risks associated with data retention. The research delves into the intricate architecture of Secure GupShup, exploring the amalgamation of cutting-edge encryption protocols and intuitive user interface design. The application's robust security measures, coupled with its user-friendly interface, position Secure GupShup as a formidable contender in the realm of secure communication applications. As the digital landscape continues to evolve, Secure GupShup signifies a pivotal step towards meeting the escalating demands for privacy and security in the realm of digital communication. The incorporation of end-to-end encryption protocols ensures that messages exchanged within Secure GupShup remain private and secure. Users can communicate confidently, knowing that their conversations are shielded from unauthorized access, providing a heightened level of privacy. The inclusion of disappearing messages adds an extra layer of security by automatically removing sensitive content after a designated time period. This feature not only protects user data from long-term exposure but also aligns with the evolving need for ephemeral communication in digital platforms. While acknowledging the current limitations, the proposed solutions aim to address these challenges and enhance the overall functionality of Secure GupShup. The outlined future work envisions continued improvements, embracing emerging technologies and user preferences to maintain Secure GupShup's position as a leading secure communication platform..

DPGazeSynth: Enhancing eye-tracking virtual reality privacy with differentially private data synthesis

As spatial computing devices increasingly integrate eye-tracking technology to enhance virtual reality (VR) experiences, the imperative to protect sensitive eye-tracking data against privacy risks, such as user re-identification, has become more pressing. Existing privacy-preserving mechanisms face challenges in balancing the dual demands of privacy and utility in the context of VR applications. This paper presents DPGazeSynth, a novel framework designed to fortify privacy protections while ensuring the utility of eye-tracking data. DPGazeSynth addresses the unique requirements of gaze path synthesis, especially the differentiation between fixations and saccades. Our approach introduces a semi-synthetic method based on the Markov Chain model to accurately maintain data correlations for analytical tasks. We demonstrate that DPGazeSynth provides robust differential privacy guarantees, and our comprehensive experiments on two real-world datasets validate its effectiveness in safeguarding against re-identification attacks. The results showcase DPGazeSynth's better performance over existing solutions like Kalεido and establish its potential as a reliable foundation for future research aimed at reconciling privacy concerns with the demands of complex trajectory data analysis in eye-tracking applications.

Adversarial Attacks on Federated-Learned Adaptive Bitrate Algorithms

Learning-based adaptive bitrate (ABR) algorithms have revolutionized video streaming solutions. With the growing demand for data privacy and the rapid development of mobile devices, federated learning (FL) has emerged as a popular training method for neural ABR algorithms in both academia and industry. However, we have discovered that FL-based ABR models are vulnerable to model-poisoning attacks as local updates remain unseen during global aggregation. In response, we propose MAFL (Malicious ABR model based on Federated Learning) to prove that backdooring the learning-based ABR model via FL is practical. Instead of attacking the global policy, MAFL only targets a single ``target client''. Moreover, the unique challenges brought by deep reinforcement learning (DRL) make the attack even more challenging. To address these challenges, MAFL is designed with a two-stage attacking mechanism. Using two representative attack cases with real-world traces, we show that MAFL significantly degrades the model performance on the target client (i.e., increasing rebuffering penalty by 2x and 5x) with a minimal negative impact on benign clients.

Multispectral camouflage and radiative cooling using dynamically tunable metasurface.

With the increasing demand for privacy, multispectral camouflage devices that utilize metasurface designs in combination with mature detection technologies have become effective. However, these early designs face challenges in realizing multispectral camouflage with a single metasurface and restricted modes. Therefore, this paper proposes a dynamically tunable metasurface. The metasurface consists of gold (Au), antimony selenide (Sb2Se3), and aluminum (Al), which enables radiative cooling, light detection and ranging (LiDAR) and infrared camouflage. In the amorphous phase of Sb2Se3, the thermal radiation reduction rate in the mid wave infrared range (MWIR) is up to 98.2%. The echo signal reduction rate for the 1064 nm LiDAR can reach 96.3%. In the crystalline phase of Sb2Se3, the highest cooling power is 65.5 Wm-2. Hence the metasurface can reduce the surface temperature and achieve efficient infrared camouflage. This metasurface design provides a new strategy for making devices compatible with multispectral camouflage and radiative cooling.

Context-Aware Hybrid Encoding for Privacy-Preserving Computation in IoT Devices

Recent years have witnessed a surge in hybrid IoT-cloud applications where an end user distributes the desired computation between the IoT and cloud nodes. While achieving significant speed up, the major caveat of this approach is data privacy. Privacy-preserving methods have received major attention in the past few years, mainly because they can potentially solve this issue. Among several proposals, methods based on dynamic encoding and perturbation offer flexibility and low overhead. However, they often consider a weak adversary model or overlook practical limitations such as encoding latency and complexity. This work proposes a new privacy-preserving method to address these issues. The key contributions of this paper are twofold. First, unlike state-of-the-art, it proposes a new approach based on evolutionary algorithms to systematically evaluate the robustness of the encoding algorithm against a large population of potential adversaries. Second, it develops a dynamic obfuscation strategy that balances latency requirements in a realistic IoT-cloud hybrid ecosystem and privacy demands. Additionally, our method offers a unique benefit: it can be used alone for privacy protection, or it can be integrated with most existing methods to enhance privacy and reduce latency. The applicability and effectiveness of our proposed methods are thoroughly evaluated using two popular deep neural networks in a real-world IoT-cloud setting. We study the impact of our approach on important metrics, such as accuracy and privacy. Our results show that our proposed method can improve the overall privacy of a given IoT-cloud hybrid ecosystem by more than 10% on average.

A Comprehensive Privacy-Preserving Federated Learning Scheme With Secure Authentication and Aggregation for Internet of Medical Things.

Data mining, integration, and utilization are the inevitable trend of the Internet of Medical Things (IoMT) in the context of Big Data. With the increasing demand for data privacy, federated learning has emerged as a new paradigm, which enables distributed joint training of medical data sources without leaving the private domain. However, federated learning is suffering from security threats as the shared local model will reveal original datasets. Privacy leakage is even more fatal in healthcare because medical data contains critically sensitive information. In addition, open wireless channels are susceptible to malicious attacks. To further safeguard the privacy of IoMT, we propose a comprehensive privacy-preserving federated learning scheme with a tactful dropout handling mechanism. The proposed scheme leverages blind masking and certificateless proxy re-encryption (CL-PRE) for secure aggregation, ensuring the confidentiality of the local model and rendering the global model invisible to any parties other than clients. It also provides authentication of uploaded models while protecting identity privacy. Compared with other relevant schemes, our solution has better performance on functional features and efficiency, and is more applicable to IoMT systems with many devices.

FADngs: Federated Learning for Anomaly Detection.

With the increasing demand for data privacy, federated learning (FL) has gained popularity for various applications. Most existing FL works focus on the classification task, overlooking those scenarios where anomaly detection may also require privacy-preserving. Traditional anomaly detection algorithms cannot be directly applied to the FL setting due to false and missing detection issues. Moreover, with common aggregation methods used in FL (e.g., averaging model parameters), the global model cannot keep the capacities of local models in discriminating anomalies deviating from local distributions, which further degrades the performance. For the aforementioned challenges, we propose Federated Anomaly Detection with Noisy Global Density Estimation, and Self-supervised Ensemble Distillation (FADngs). Specifically, FADngs aligns the knowledge of data distributions from each client by sharing processed density functions. Besides, FADngs trains local models in an improved contrastive learning way that learns more discriminative representations specific for anomaly detection based on the shared density functions. Furthermore, FADngs aggregates capacities by ensemble distillation, which distills the knowledge learned from different distributions to the global model. Our experiments demonstrate that the proposed method significantly outperforms state-of-the-art federated anomaly detection methods. We also empirically show that the shared density function is privacy-preserving. The code for the proposed method is provided for research purposes https://github.com/kanade00/Federated_Anomaly_detection.

Willingness-to-Pay for Privacy: A Study on IPhone

This research employs the iPhone as a case study and conducts a survey to investigate consumers' willingness-to-pay (WTP) for privacy. We discover that consumers not only value their privacy in the digital realm but are also willing to invest considerably in privacy-protection services. What makes our findings particularly intriguing is the relationship between consumers' privacy habits and their willingness-to-pay. Surprisingly, individuals who may exhibit lower vigilance in safeguarding their privacy online paradoxically demonstrate a higher willingness-to-pay for services that ensure their data protection. Our research not only sheds light on consumer behavior but also provides invaluable strategic implications for businesses, especially tech giants like Apple. By leveraging privacy protection as a key selling point, firms can not only cater to the increasing concerns of privacy-conscious consumers but also elevate their products to a new level of desirability. Effectively communicating the robust privacy features of products like the iPhone empowers companies to justify premium pricing, enhancing their revenue streams while simultaneously fostering a safer digital environment for their users. This study stands as a testament to the symbiotic relationship between consumer demand for privacy and businesses' strategic innovation, driving the evolution of both technology and user protection in the digital age.

Verification and Enforcement of (ϵ, ξ)-Differential Privacy over Finite Steps in Discrete Event Systems

In the realm of data protection strategies, differential privacy ensures that unauthorized entities cannot reconstruct original data from system outputs. This study explores discrete event systems, specifically through probabilistic automata. Central is the protection of state data, particularly the initial state privacy of multiple starting states. We introduce an evaluation criterion to safeguard initial states. Using advanced algorithms, the proposed method counters the probabilistic identification of any state within this collection by adversaries from observed data points. The efficacy is confirmed when the probability distributions of data observations tied to these states converge. If a system’s architecture does not meet state differential privacy demands, we propose an enhanced supervisory control mechanism. This control upholds state differential privacy across all initial states, maintaining operational flexibility within the probabilistic automaton framework. Concluding, a numerical analysis validates the approach’s strength in probabilistic automata and discrete event systems.

Designing for passengers' information needs on fellow travelers: A comparison of day and night rides in shared automated vehicles

Shared automated mobility-on-demand promises efficient, sustainable, and flexible transportation. Nevertheless, security concerns, resilience, and their mutual influence – especially at night – will likely be the most critical barriers to public adoption since passengers have to share rides with strangers without a human driver on board. Prior research points out that having information about fellow travelers could alleviate the concerns of passengers and we designed two user interface variants to investigate the role of this information in an exploratory within-subjects user study (N=24). Participants experienced four automated day and night rides with varying personal information about co-passengers in a simulated environment. The results of the mixed-method study indicate that having information about other passengers (e.g., photo, gender, and name) positively affects user experience at night. In contrast, it is less necessary during the day. Considering participants' simultaneously raised privacy concerns, balancing security and privacy demands poses a substantial challenge for resilient system design.

Toward Cooperative Federated Learning Over Heterogeneous Edge/Fog Networks

Federated learning (FL) has been promoted as a popular technique for training machine learning (ML) models over edge/fog networks. Traditional implementations of FL have largely neglected the potential for inter-network cooperation, treating edge/fog devices and other infrastructure participating in ML as separate processing elements. Consequently, FL has been vulnerable to several dimensions of network heterogeneity, such as varying computation capabilities, communication resources, data qualities, and privacy demands. We advocate for cooperative federated learning (CFL), a cooperative edge/fog ML paradigm built on device-to-device (D2D) and device-toserver (D2S) interactions. Through D2D and D2S cooperation, CFL counteracts network heterogeneity in edge/fog networks through enabling a model/data/resource pooling mechanism, which will yield substantial improvements in ML model training quality and network resource consumption. We propose a set of core methodologies that form the foundation of D2D and D2S cooperation and present preliminary experiments that demonstrate their benefits. We also discuss new FL functionalities enabled by this cooperative framework such as the integration of unlabeled data and heterogeneous device privacy into ML model training. Finally, we describe some open research directions at the intersection of cooperative edge/fog and FL.

Analyze the Impact of the Digital RMB on International Payments and Financial Structure Based on Literature Review

With the development of network technology and digital economy, the demand for convenience, security, inclusiveness and privacy of retail payment is increasing. Central banks or monetary authorities in many countries and regions are actively exploring ways to digitize fiat money to meet this demand. The Chinese government has decided to introduce digital RMB as a digital form of legal tender to enhance the payment experience, strengthen financial supervision, promote economic transformation and financial technology innovation and application. The introduction of digital RMB helps to reduce the cost and risk of traditional cash flow, promote the upgrading of payment methods and the development of digital economy. Based on the comprehensive and systematic collection, collation and analysis of the relevant literature in the field of digital RMB, this paper explores the impact of digital RMB on international payment and financial structure. The study found that digital RMB has international security as a payment tool, which can promote cross-border transactions and change the global payment system. Digital RMB can enhance international financial security and affect the implementation of financial policies in various countries.

AI, marketing technology and personalisation at scale

From increased consumer demand for privacy and the deprecation of third-party cookies to expectations for more relevant and personalised advertising, the dynamics of the digital advertising industry are quickly evolving. Marketing technology today is challenged with bringing the digital ecosystem together responsibly, aligning what consumers want with what brands can offer in real time. Artificial intelligence (AI) has proven to be an effective tool for synthesising the data needed to accomplish these tasks and deliver results. How exactly is AI bridging the gap between attitudes about privacy and the need for personalisation at scale? What role do deterministic and probabilistic data have in creating a more trustworthy and relevant digital experience? And how can marketers use AI to respond to real-time events and shifts in consumer preferences? This paper will address how AI can help marketers achieve scale without third-party cookies, and why consumer preferences, varied types of data and real-time measurement are central to achieving personalisation in advertising today.

Enterprise Architecture Design for the Transformation of Online Financial Services

In the ever-expanding digital era, online financial services have emerged as a significant component of the global financial ecosystem. Digital transformation has altered the operations and consumer interactions of financial services companies. By implementing Enterprise Architecture design, financial services companies can adapt, flourish, and compete in an increasingly competitive environment. This article examines the essential function of Enterprise Architecture in the evolution of online financial services. We explore how Enterprise Architecture provides a strategic framework for driving change and innovation in financial organizations. We examine the primary elements of an Enterprise Architecture, such as the business model, technology infrastructure, and data. Then, we investigate how Enterprise Architecture improves operational efficiency and decreases risk in digital financial services. In addition, the significance of security and conformance in Enterprise Architecture for online financial services is examined. Enterprise Architecture is crucial in integrating security solutions and ensuring regulatory compliance in light of rising cybersecurity threats, strict regulatory compliance, and consumer demands for data privacy. We discuss how Enterprise Architecture facilitates improved consumer experiences in online financial services. By designing solutions with the consumer in mind, businesses can meet customer expectations, increase customer retention, and build a sustainable market share. Enterprise Architecture has become an indispensable instrument for successfully transforming online financial services. Through strategic planning, technology integration, data management, and a focus on security and customer experience, financial institutions can meet the challenges and seize the opportunities presented by the digital era.

P2FLF: Privacy-Preserving Federated Learning Framework Based on Mobile Fog Computing

Mobile IoT devices provide a lot of data every day, which provides a strong base for machine learning to succeed. However, the stringent privacy demands associated with mobile IoT data pose significant challenges for its implementation in machine learning tasks. In order to address this challenge, we propose privacy-preserving federated learning framework (P2FLF) in a mobile fog computing environment. By employing federated learning, it is possible to bring together numerous dispersed user sets and collectively train models without the need to upload datasets. Federated learning, an approach to distributed machine learning, has garnered significant attention for its ability to enable collaborative model training without the need to share sensitive data. By utilizing fog nodes deployed at the edge of the network, P2FLF ensures that sensitive mobile IoT data remains local and is not transmitted to the central server. The framework integrates privacy-preserving methods, such as differential privacy and encryption, to safeguard the data throughout the learning process. We evaluate the performance and efficacy of P2FLF through experimental simulations and compare it with existing approaches. The results demonstrate that P2FLF strikes a balance between model accuracy and privacy protection while enabling efficient federated learning in mobile IoT environments.

Managing Human Capital with Employee Clustering Through the Interplay of the Persona Concept

Nowadays, employees are seen as active Human Capital Management (HCM) consumers. Megatrends such as individualization and flexible work impact the workforce, especially generations Y and Z. Finding and retaining talent is a challenge that companies try to address, from employer branding to covering the whole lifespan of the company-employee relationship. Management is likely to know every employee in small and medium-sized enterprises (SMEs); however, larger companies need to categorize their human capital into certain entities. This paper explores the usability of the Persona concept for HCM. Persona is a service design approach widely applied in marketing, and a few research papers have argued that personas may have a role in improved people-oriented HCM. In short, personas aim to create a limited set of archetypes that differ from each other regarding demo- and psychographics. Generally, they are a mix of real-life people and some imaginary attributes. Different personas will likely request different services to feel fully motivated and supported. The core target of this article was to identify the common awareness of personas among HCM experts across industries and countries. Also, the ideation of future use of Personas was studied with marketing experts actively using personas in their professions. The qualitative study based on twelve semi-structured individual interviews was conducted in November-December 2022. The results indicate that the HCM community is well aware of the persona concept, which is widely used. However, the current usage of personas is mainly focused on employer branding and recruitment, and only in a few cases the usage covered the design of internal HCM processes. The study also revealed some limitations for HCM that are not present in the marketing field. Demands for transparency, equality, data privacy, and personal integrity set challenges in clustering the workforce. The results indicate the further need for research on best practices of creating and using Personas in a way that ethically serves the interests of both the company and the employee.

MQTT-A: A Broker-Bridging P2P Architecture to Achieve Anonymity in MQTT

The demand for privacy in the current digital era is continuously growing. This is particularly true in the context of IoT, in which huge amounts of data are handled. Communication anonymity is a fundamental requirement when high privacy levels should be guaranteed. On the other hand, very little attention has been devoted to this problem in the past scientific literature, when referring to MQTT, which is the de-facto standard for IoT communication. In this paper, we try to cover this gap. Specifically, we propose a new protocol, called MQTT-A, which extends the MQTT bridging mechanism to support the anonymity of both publishers and subscribers. This task is accomplished through the P2P collaboration of intermediate bridge brokers, which forward the requests of clients so that the final broker cannot understand the actual source/destination. Moreover, an anonymity-preserving topic discovery mechanism is provided, which allows clients to discover available topics and associated brokers, preventing client identification. Importantly, all the MQTT-A messages are exchanged by leveraging standard MQTT primitives and the bridging mechanism natively offered by MQTT. This allows us not to require changes in the standard MQTT infrastructure. To validate the performance of our solution, we performed a deep experimental campaign by deploying the bridge brokers on cloud platforms in various countries of the world. The experimental validation shows that, the price of latency we have to pay because of the trade-off with anonymity is quite reasonable. Moreover, no significant impact on goodput occurs in the case of good network conditions.

Efficient and Privacy-Preserving Spatial Keyword Similarity Query Over Encrypted Data

As a popular and practical query type in location-based services, the spatial keyword query has been extensively studied in both academia and industry. Meanwhile, with the growing demand for data privacy, many privacy-preserving spatial keyword query schemes have been proposed to deal with queries over encrypted data. However, none of the existing schemes preserve access pattern privacy, and the recent research illustrates that leaking such privacy may incur inference attacks and thus disclose sensitive information. In addition, most existing schemes only consider the boolean keyword search, which is not quite practical and flexible in real-world applications. To address the above issues, in this paper, we propose two privacy-preserving spatial keyword similarity query schemes that can preserve full and partial access pattern privacy, respectively. First, we present a basic privacy-preserving spatial keyword similarity query scheme (PPSKS) by integrating a secure set membership test (SSMT) technique with secure circuits. After that, to improve performance, we propose a tree-based scheme (PPSKS+) by employing a new index called FR-tree together with a predicate encryption technique that can encrypt FR-tree. Formal security analysis shows that: i) our proposed schemes can protect outsourced data, query requests, and query results; ii) our PPSKS scheme can hide full access patterns, while the PPSKS+ scheme preserves <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$m$</tex-math></inline-formula> -access pattern privacy. Extensive experiments are also conducted, and the results indicate that our tree-based PPSKS+ scheme is much more efficient, almost two orders of magnitude better than our linear search PPSKS scheme in performing queries.