User Privacy Preferences Research Articles

A User-Centered Privacy Policy Management System for Automatic Consent on Cookie Banners

Despite growing concerns about privacy and an evolution in laws protecting users’ rights, there remains a gap between how industries manage data and how users can express their preferences. This imbalance often favors industries, forcing users to repeatedly define their privacy preferences each time they access a new website. This process contributes to the privacy paradox. We propose a user support tool named the User Privacy Preference Management System (UPPMS) that eliminates the need for users to handle intricate banners or deceptive patterns. We have set up a process to guide even a non-expert user in creating a standardized personal privacy policy, which is automatically applied to every visited website by interacting with cookie banners. The process of generating actions to apply the user’s policy leverages customized Large Language Models. Experiments demonstrate the feasibility of analyzing HTML code to understand and automatically interact with cookie banners, even implementing complex policies. Our proposal aims to address the privacy paradox related to cookie banners by reducing information overload and decision fatigue for users. It also simplifies user navigation by eliminating the need to repeatedly declare preferences in intricate cookie banners on every visited website, while protecting users from deceptive patterns.

Data Sharing Privacy Metrics Model Based on Information Entropy and Group Privacy Preference

With the development of the mobile internet, service providers obtain data and resources through a large number of terminal user devices. They use private data for business empowerment, which improves the user experience while causing users’ privacy disclosure. Current research ignores the impact of disclosing user non-sensitive attributes under a single scenario of data sharing and lacks consideration of users’ privacy preferences. This paper constructs a data-sharing privacy metrics model based on information entropy and group privacy preferences. Use information theory to model the correlation of the privacy metrics problem, the improved entropy weight algorithm to measure the overall privacy of the data, and the analytic hierarchy process to correct user privacy preferences. Experiments show that this privacy metrics model can better quantify data privacy than conventional methods, provide a reliable evaluation mechanism for privacy security in data sharing and publishing scenarios, and help to enhance data privacy protection.

Enhancing user awareness on inferences obtained from fitness trackers data.

In the IoT era, sensitive and non-sensitive data are recorded and transmitted to multiple service providers and IoT platforms, aiming to improve the quality of our lives through the provision of high-quality services. However, in some cases these data may become available to interested third parties, who can analyse them with the intention to derive further knowledge and generate new insights about the users, that they can ultimately use for their own benefit. This predicament raises a crucial issue regarding the privacy of the users and their awareness on how their personal data are shared and potentially used. The immense increase in fitness trackers use has further increased the amount of user data generated, processed and possibly shared or sold to third parties, enabling the extraction of further insights about the users. In this work, we investigate if the analysis and exploitation of the data collected by fitness trackers can lead to the extraction of inferences about the owners routines, health status or other sensitive information. Based on the results, we utilise the PrivacyEnhAction privacy tool, a web application we implemented in a previous work through which the users can analyse data collected from their IoT devices, to educate the users about the possible risks and to enable them to set their user privacy preferences on their fitness trackers accordingly, contributing to the personalisation of the provided services, in respect of their personal data.

A Study of Users' Privacy Preferences for Data Sharing on Symptoms-Tracking/Health App.

Symptoms-tracking applications allow crowdsensing of health and location related data from individuals to track the spread and outbreaks of infectious diseases. During the COVID-19 pandemic, for the first time in history, these apps were widely adopted across the world to combat the pandemic. However, due to the sensitive nature of the data collected by these apps, serious privacy concerns were raised and apps were critiqued for their insufficient privacy safeguards. The Covid Nearby project was launched to develop a privacy-focused symptoms-tracking app and to understand the privacy preferences of users in health emergencies. In this work, we draw on the insights from the Covid Nearby users' data, and present an analysis of the significantly varying trends in users' privacy preferences with respect to demographics, attitude towards information sharing, and health concerns, e.g. after being possibly exposed to COVID-19. These results and insights can inform health informatics researchers and policy designers in developing more socially acceptable health apps in the future.

A Trust-Driven Contract Incentive Scheme for Mobile Crowd-Sensing Networks

By leveraging the power of crowd, the prevalence of mobile devices in mobile crowd-sensing (MCS) networks helps and provides a wide range of sensing services through collecting and sharing sensing data. However, due to the diverse behaviours of mobile users, unreliable or malicious users and platforms could provide untrusted data, which affects the quality of sensing service. Besides, mobile users are reluctant to participate in sensing tasks without sufficient incentives. It is desirable to design a trust and incentive scheme to improve the service efficiency of MCS. In this paper, we propose a novel trust-driven contract incentive framework in MCS, which guarantees the service quality and stimulates mobile users to join sensing tasks. We first design a trust evaluation scheme between mobile users and sensing platforms based on the historical interactions to derive the reliability value of sensing platform. Then, the trust threshold is formulated to filter out malicious sensing platforms. By considering the privacy preferences of mobile users, we establish a contract incentive scheme to maximize the utility of both mobile users and sensing platforms. The design objective is to derive a set of optimal contracts under both discrete and continuous contract models. Meanwhile, the designed contracts guarantee the individual rationality (IR) and incentive compatibility (IC) properties. Finally, simulations are conducted to evaluate the effectiveness of the proposed trust-driven contract incentive scheme, and results demonstrate that the proposed scheme can jointly improve the quality of sensing service and maximize the utilities of mobile users and sensing platforms.

Incorporating individual and group privacy preferences in the internet of things

This paper presents a new privacy negotiation mechanism for an IoT environment that is both efficient and practical to cope with the IoT special need of seamlessness. This mechanism allows IoT users to express and enforce their personal privacy preferences in a seamless manner while interacting with IoT deployments. A key contribution of the paper is that it addresses the privacy concerns of individual users as well as a group of users where privacy preferences of all individual users are combined into a group privacy profile to be negotiated with the IoT owner. In addition, the proposed mechanism satisfies the privacy requirements of the IoT deployment owner. Finally, the proposed privacy mechanism is agnostic to the actual IoT architecture and can be used over a user-managed, edge-managed or a cloud-managed IoT architecture. Prototypes of the proposed mechanism have been implemented for each of these three architectures, and the results show the capability of the protocol to negotiate privacy while adding insignificant time overhead.

Privacy-Aware Personal Data Storage (P-PDS): Learning how to Protect User Privacy from External Applications

Recently, Personal Data Storage (PDS) has inaugurated a substantial change to the way people can store and control their personal data, by moving from a service-centric to a user-centric model. PDS offers individuals the capability to keep their data in a unique logical repository, that can be connected and exploited by proper analytical tools, or shared with third parties under the control of end users. Up to now, most of the research on PDS has focused on how to enforce user privacy preferences and how to secure data when stored into the PDS. In contrast, in this paper we aim at designing a Privacy-aware Personal Data Storage (P-PDS), that is, a PDS able to automatically take privacy-aware decisions on third parties access requests in accordance with user preferences. The proposed P-PDS is based on preliminary results presented in [1] , where it has been demonstrated that semi-supervised learning can be successfully exploited to make a PDS able to automatically decide whether an access request has to be authorized or not. In this paper, we have deeply revised the learning process in order to have a more usable P-PDS, in terms of reduced effort for the training phase, as well as a more conservative approach w.r.t. users privacy, when handling conflicting access requests. We run several experiments on a realistic dataset exploiting a group of 360 evaluators. The obtained results show the effectiveness of the proposed approach.

A recommendation approach for user privacy preferences in the fitness domain

Fitness trackers are undoubtedly gaining in popularity. As fitness-related data are persistently captured, stored, and processed by these devices, the need to ensure users’ privacy is becoming increasingly urgent. In this paper, we apply a data-driven approach to the development of privacy-setting recommendations for fitness devices. We first present a fitness data privacy model that we defined to represent users’ privacy preferences in a way that is unambiguous, compliant with the European Union’s General Data Protection Regulation (GDPR), and able to represent both the user and the third party preferences. Our crowdsourced dataset is collected using current scenarios in the fitness domain and used to identify privacy profiles by applying machine learning techniques. We then examine different personal tracking data and user traits which can potentially drive the recommendation of privacy profiles to the users. Finally, a set of privacy-setting recommendation strategies with different guidance styles are designed based on the resulting profiles. Interestingly, our results show several semantic relationships among users’ traits, characteristics, and attitudes that are useful in providing privacy recommendations. Even though several works exist on privacy preference modeling, this paper makes a contribution in modeling privacy preferences for data sharing and processing in the IoT and fitness domain, with specific attention to GDPR compliance. Moreover, the identification of well-identified clusters of preferences and predictors of such clusters is a relevant contribution for user profiling and for the design of interactive recommendation strategies that aim to balance users’ control over their privacy permissions and the simplicity of setting these permissions.

A generalization model for multi-record privacy preservation

Privacy preservation becomes a more and more serious problem in data publication, which has drawn dramatic attention in research and development. Recently, several privacy preservation models and algorithms have been proposed for publishing data. However, most of the previous methods suffer from more than one drawback as follows: (i) Could not be used on multi-record datasets. (ii) Only guarantee one-way generalization. (iii) User privacy preferences are ignored. In order to satisfy higher privacy requirements and make it suitable for multi-record publishing datasets, a bidirectional personalized generalization (BP-generalization) model is proposed as a new solution in this paper. The rational is to focus anonymous objects on both relational and set-valued information. First, we merge tuples with the same attribute values in multi-record datasets to ensure the validity of quasi-identifier anonymity. Second, by enforcing l-diversity on equivalence groups and k-anonymity on fingerprint buckets respectively, privacy preservation model may resist bi-directional chain attack. Finally, a new hierarchical generalization strategy is also proposed for personal privacy preservation of sensitive attributes, then different generalization rules can be adopted for different levels of sensitive values. Extensive experimental results on two datasets show that the performance of our method is better than state-of-art techniques in terms of efficiency and information loss.

Mobi-SAGE-RS: A sparse additive generative model-based mobile application recommender system

With the rapid prevalence of smart mobile devices and the dramatic proliferation of mobile applications (Apps), App recommendation becomes an emergent task that will benefit different stockholders of mobile App ecosystems. However, the extreme sparsity of user-App matrix and many newly emerging Apps create severe challenges, causing CF-based methods to degrade significantly in their recommendation performance. Besides, unlike traditional items, Apps have rights to access users’ personal resources (e.g., location, message and contact) which may lead to security risk or privacy leak. Thus, users’ choosing of Apps are influenced by not only their personal interests but also their privacy preferences. Moreover, user privacy preferences vary with App categories. In light of the above challenges, we propose a mobile sparse additive generative model (Mobi-SAGE) to recommend Apps by considering both user interests and category-aware user privacy preferences in this paper. To overcome the challenges from data sparsity and cold start, Mobi-SAGE exploits both textual and visual content associated with Apps to learn multi-view topics for user interest modeling. We collected a large-scale and real-world dataset from 360 App store - the biggest Android App platform in China, and conducted extensive experiments on it. The experimental results demonstrate that our Mobi-SAGE consistently and significantly outperforms the other existing state-of-the-art methods, which implies the importance of exploiting category-aware user privacy preferences and the multi-modal App content data on personalized App recommendation.

A Blockchain Connected Gateway for BLE-Based Devices in the Internet of Things

Recently, the popularity of the Internet of Things (IoT) has led to a rapid development and significant advancement of ubiquitous applications seamlessly integrated within our daily life. Owing to the accompanying growth of the importance of privacy, a great deal of attention has focused on the issues of secure management and robust access control of IoT devices. In this paper, we propose the design of a blockchain connected gateway which adaptively and securely maintains user privacy preferences for IoT devices in the blockchain network. Individual privacy leakage can be prevented because the gateway effectively protects users’ sensitive data from being accessed without their consent. A robust digital signature mechanism is proposed for the purposes of authentication and secure management of privacy preferences. Furthermore, we adopt the blockchain network as the underlying architecture of data processing and maintenance to resolve privacy disputes.

A privacy self-assessment framework for online social networks

During our digital social life, we share terabytes of information that can potentially reveal private facts and personality traits to unexpected strangers. Despite the research efforts aiming at providing efficient solutions for the anonymization of huge databases (including networked data), in online social networks the most powerful privacy protection “weapons” are the users themselves. However, most users are not aware of the risks derived by the indiscriminate disclosure of their personal data. Moreover, even when social networking platforms allow their participants to control the privacy level of every published item, adopting a correct privacy policy is often an annoying and frustrating task and many users prefer to adopt simple but extreme strategies such as “visible-to-all” (exposing themselves to the highest risk), or “hidden-to-all” (wasting the positive social and economic potential of social networking websites). In this paper we propose a theoretical framework to i) measure the privacy risk of the users and alert them whenever their privacy is compromised and ii) help the users customize semi-automatically their privacy settings by limiting the number of manual operations. By investigating the relationship between the privacy measure and privacy preferences of real Facebook users, we show the effectiveness of our framework.

Resolving Multi-Party Privacy Conflicts in Social Media

Items shared through Social Media may affect more than one user's privacy --- e.g., photos that depict multiple users, comments that mention multiple users, events in which multiple users are invited, etc. The lack of multi-party privacy management support in current mainstream Social Media infrastructures makes users unable to appropriately control to whom these items are actually shared or not. Computational mechanisms that are able to merge the privacy preferences of multiple users into a single policy for an item can help solve this problem. However, merging multiple users' privacy preferences is not an easy task, because privacy preferences may conflict, so methods to resolve conflicts are needed. Moreover, these methods need to consider how users' would actually reach an agreement about a solution to the conflict in order to propose solutions that can be acceptable by all of the users affected by the item to be shared. Current approaches are either too demanding or only consider fixed ways of aggregating privacy preferences. In this paper, we propose the first computational mechanism to resolve conflicts for multi-party privacy management in Social Media that is able to adapt to different situations by modelling the concessions that users make to reach a solution to the conflicts. We also present results of a user study in which our proposed mechanism outperformed other existing approaches in terms of how many times each approach matched users' behaviour.

The continuing problems with online consent under the EU's emerging data protection principles

Since the original Data Protection Directive in 1995, EU law has attached particular importance to user consent. This emphasis on consent is retained – albeit in different forms – in the various positions adopted by the EU institutions on the draft General Data Protection Regulation in advance of their trilogue negotiations. This article identifies three distinct models of user consent in the EU jurisprudence in this area: presumed consent; informed consent; and active consent. The article suggests that the later models developed as a response to empirical concerns about treating consent as a reliable proxy for user privacy preferences online. On this analysis, the active consent model advocated by the Article 29 Working Party and favoured by the Parliament's draft Data Protection Regulation is assumed to address the empirical issues associated with the presumed and informed consent models. In fact, the psychology and behavioural science research shows that website users are subject to a variety of specific situational influences that intuitively impel the giving of consent. The article concludes that the EU's approach to data and privacy protection online, even under current proposals, is fundamentally misguided and makes four recommendations about the future direction of EU law in this area.

Framing and Measuring Multi-Dimensional Interpersonal Privacy Preferences of Social Networking Site Users

Framing and Measuring Multi-Dimensional Interpersonal Privacy Preferences of Social Networking Site Users

Transforming grid to cloud services for multimodal biometrics

Various barriers due to performance, security and privacy issues associated with shared heterogeneous data centres impede the diffusion of multimodal biometrics that could enhance secured systems in our everyday lives. This paper identifies the issues associated with multimodal fusion and privacy risks and provides means of addressing them. It presents the need for transforming from grid-based services to cloud-based services in order to address these issues, paving the way towards a successful user acceptance of biometric systems. Though grid services could provide heterogeneous resource sharing capabilities and security, multimodal biometrics require scalability and pricing flexibility for gaining user-acceptance and easy adoption. By leveraging 'on-the-demand' services offered in cloud computing, reliable and context-sensitive privacy policies could be applied for multimodal biometrics to cater to the different business and user requirements. Further, the paper combines the concept of cloud services along with momentum back propagation induced artificial neural network MBP-ANN model of machine learning for efficient multimodal fusion and flexible authentication schemes. The paper also describes a case scenario of how this model could be adopted by applying the varying security requirements and user privacy preferences for a successful diffusion of multimodal biometrics in the cloud.

Mapping User Preference to Privacy Default Settings

Managing the privacy of online information can be a complex task often involving the configuration of a variety of settings. For example, Facebook users determine which audiences have access to their profile information and posts, how friends can interact with them through tagging, and how others can search for them—and many more privacy tasks. In most cases, the default privacy settings are permissive and appear to be designed to promote information sharing rather than privacy. Managing privacy online can be complex and often users do not change defaults or use granular privacy settings. In this article, we investigate whether default privacy settings on social network sites could be more customized to the preferences of users. We survey users' privacy attitudes and sharing preferences for common SNS profile items. From these data, we explore using audience characterizations of profile items to quantify fit scores that indicate how well default privacy settings represent user privacy preferences. We then explore the fit of various schemes, including examining whether privacy attitude segmentation can be used to improve default settings. Our results suggest that using audience characterizations from community data to create default privacy settings can better match users' desired privacy settings.

Understanding Sensitivity by Analyzing Anonymity [Guest editor's introduction

We can infer user privacy preferences and expectations by observing how people use existing product features. An analysis of how users employ anonymity features on Quora, a question-and-answer site, shows that the range of topics they consider sensitive is much broader than what service providers or regulators typically deem sensitive. A data-driven approach can help online services improve their products by developing features that let users express and exercise privacy preferences more effectively.

The Application of SWRL Based Ontology Inference for Privacy Protection

The phenomena of illegal disclosure of user privacy has become more serious considerably in the last years. How to protect user privacy and prevent user privacy from illegal disclosure has become of great interest to researchers. In this paper, we propose an approach of user privacy protection based on ontology inference. We create privacy ontology through detailed analysis of privacy domain. Then, we reason the privacy ontology with Semantic Web Rule Language (SWRL) rule of user privacy preference. By creating ontology and SWRL rules, then reasoning logically, we can find adequate service whose privacy policy coincides with user privacy preference and protect user privacy effectively. The result of experiment shows that the approach proposed in this paper is feasible and effective.

Service Outsourcing Character Oriented Privacy Conflict Detection Method in Cloud Computing

Cloud computing has provided services for users as a software paradigm. However, it is difficult to ensure privacy information security because of its opening, virtualization, and service outsourcing features. Therefore how to protect user privacy information has become a research focus. In this paper, firstly, we model service privacy policy and user privacy preference with description logic. Secondly, we use the pellet reasonor to verify the consistency and satisfiability, so as to detect the privacy conflict between services and user. Thirdly, we present the algorithm of detecting privacy conflict in the process of cloud service composition and prove the correctness and feasibility of this method by case study and experiment analysis. Our method can reduce the risk of user sensitive privacy information being illegally used and propagated by outsourcing services. In the meantime, the method avoids the exception in the process of service composition by the privacy conflict, and improves the trust degree of cloud service providers.