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Smartphones have become essential for functioning in society, but as more personal information is accessed, transferred, or stored on smartphones, users struggle to control the release of their information via privacy settings. To enhance their privacy, individuals must be knowledgeable about their smartphone and motivated to use the device’s settings. Therefore, we explore the roles of knowledge and motivation in affecting smartphone owners’ use of settings to limit sharing of location-based information. The authors find that personal motivation is the strongest factor affecting such use, and the opinions of others do not matter. This is likely because of the personal nature of smartphones. Furthermore, privacy knowledge and individuals’ perceptions of their abilities to use privacy settings also affect this usage. However, a privacy knowledge–belief gap exists by which people with high levels of privacy knowledge utilize less restrictive privacy settings when their confidence in protecting themselves is low. The combined lack of effect from social motivation and the importance of perceived and actual privacy knowledge suggest that asking parents, teachers, or “important” others to tell individuals how to better protect themselves is unlikely to give the intended results. Instead, we need to appeal to individuals’ personal motivation and offer them training via experiential learning, such as games or educational apps. The omnipresence of smartphones means that more and more personal information is accessed, transferred, or stored on these devices. Smartphone users struggle to control the release of their information when smartphones are always connected, close at hand, and the privacy settings for individual apps are difficult to access. To have meaningful privacy in this context, individuals must be knowledgeable about their devices and truly motivated to make use of the device’s privacy settings. We draw from extant privacy literature, the self-efficacy theory, and the information–motivation–behavioral skills model to understand usage of privacy settings on smartphones through data from 334 iPhone users. Our findings indicate that personal motivation is one of the strongest determinants of utilizing privacy-protective settings, and social motivation is not significant. Furthermore, privacy knowledge and self-efficacy constructs (i.e., knowledge specific to the device’s privacy settings) determine one’s use of privacy-protective settings, but knowledge and self-efficacy about smartphone technology do not. An interaction effect also exists between privacy knowledge and privacy self-efficacy such that people with high levels of privacy knowledge utilize less restrictive privacy settings when their confidence in protecting themselves is low, but as their self-efficacy increases, they are more likely to use more privacy-protective settings. We label this the privacy knowledge–belief gap.

PurposeWhile research on privacy concerns is rich in understanding and depth, there is still not a clear understanding of why people express having privacy concerns, but do not behave consistently with their concern. We propose that this misalignment derives from a diverse set of privacy boundaries, depending on the user. This research builds on prior Communication Privacy Management Theory research to further define individual privacy boundaries. Beyond that, we evaluate the relationship between the privacy boundaries people set, and their ability to protect themselves.Design/methodology/approachA survey was conducted to assess how private individuals find twenty items. Along with measuring the sensitivity of information, we collected responses on the Online Privacy Information Literacy test to measure differences in sensitivity based on privacy knowledge. 285 participant’s responses were evaluated using exploratory factor analysis and K-means clustering.FindingsWe identify five different groups of privacy indicators. Our findings also suggest that users have limited understanding of how to keep data private, even if they have high privacy concerns.Originality/valueWe contribute to theory by offering guidance on how to better apply theoretical understanding, based on our results. More explicitly, we offer analysis that suggests boundary conditions might be absent from current theoretical understanding. Practically, we offer guidance for understanding privacy differences, which is important to understanding how to implement privacy protection laws.

The pervasiveness of dialogue systems and virtual conversation applications raises an important theme: the potential of sharing sensitive information, and the consequent need for protection. To guarantee the subject’s right to privacy, and avoid the leakage of private content, it is important to treat sensitive information. However, any treatment requires firstly to identify sensitive text, and appropriate techniques to do it automatically. The Sensitive Information Detection (SID) task has been explored in the literature in different domains and languages, but there is no common benchmark. Current approaches are mostly based on artificial neural networks (ANN) or transformers based on them. Our research focuses on identifying categories of personal data in informal English sentences, by adopting a new logical-symbolic approach, and eventually hybridising it with ANN models. We present a frame-based knowledge graph built for personal data categories defined in the Data Privacy Vocabulary (DPV). The knowledge graph is designed through the logical composition of already existing frames, and has been evaluated as background knowledge for a SID system against a labeled sensitive information dataset. The accuracy of PRIVAFRAME reached 78%. By comparison, a transformer-based model achieved 12% lower performance on the same dataset. The top-down logical-symbolic frame-based model allows a granular analysis, and does not require a training dataset. These advantages lead us to use it as a layer in a hybrid model, where the logical SID is combined with an ANNs SID tested in a previous study by the authors.

Data are collected and regarded as valuable assets in many business domains. Their owner would not want to disclose them to the public due to their potential value. Distributed knowledge discovery techniques have been proposed which assume the cooperation of data owners even though they might not behave in a trustworthy manner. When a party decides to quit the cooperation in the distributed knowledge discovery, the other parties cannot continue the discovery task and hence they get some disadvantage due to the party’s betrayal. This paper is concerned with data privacy-preserving distributed knowledge discovery which gives penalty to the party who quits the cooperation in the discovery process. It proposes a blockchain-based distributed machine learning method which does not disclose the participating parties’ data and gives the penalty to betraying parties. The proposed method makes the participating parties communicate with each other via the smart contract on the blockchain network. It uses a blockchain-based incentive system to establish trust among parties and to improve the quality of discovery knowledge. The proposed method has been implemented with a smart contract on the blockchain and tested for a benchmark data.

This paper proposes a process-aware, tag-based digital identity framework that enhances interoperability while enabling identity unlinkability and selective disclosure across multi-party workflows involving sensitive data. We realize this framework within the self-sovereign identity (SSI) paradigm, employing zk-SNARK–based zero-knowledge proofs to enable verifiable identity authentication without plaintext disclosure. The framework introduces a protocol-tagging mechanism to support multiple proof systems within a unified architecture, thereby enhancing SSI scalability and interoperability. Its core innovation lies in combining identity unlinkability and process-driven data disclosure: derived sub-identities mitigate identity-linkage attacks, while layered encryption enables selective, stepwise decryption of sensitive information (e.g., delivery addresses), ensuring participants access only the minimal information necessary for their tasks. In addition, zero-knowledge proof-based verification guarantees that the validation of derived sub-identities can be performed without sharing any plaintext attributes or identifying factors. We applied the framework to logistics, where sub-identities anonymize participants and layered encryption allows for delivery addresses to be decrypted progressively along the logistics chain, with only the final courier authorized to access complete information. During the parcel receipt process, users can complete verification using derived sub-identities and zero-knowledge proofs alone, without disclosing any real personal information or attributes that could be linked back to their identity. Trusted Execution Environments (TEEs) ensure the authenticity of decryption requests, while blockchain provides immutable audit trails. A demonstration system was implemented, formally verified using Scyther, and performance-tested across multiple platforms, including resource-constrained environments, showing high efficiency and strong practical potential. The core paradigms of identity unlinkability and process-driven data disclosure are generalizable and applicable to multi-party scenarios involving sensitive data flows.

Privacy affects every user who exchanges information over the Internet. In the past few years, the growth of information on social networks (such as Facebook, Twitter, LinkedIn) has increased exponentially. Companies are harvesting this information with and without the knowledge of individuals. While the exchange of information and seamless interaction between individuals and groups has become an easy task, issues related to this exchange, such as information privacy and security, have created new challenges. This study investigated respondents' attitudes towards privacy on social networking sites. In addition, the study sought to ascertain whether socio-demographic variables and knowledge of privacy issues influence attitudes and privacy concerns towards using social computing sites. Data analysis includes descriptive profile analysis, and statistical validation of attitudes and privacy concerns by means of correlation, regression, and cluster analysis. There was a significant relationship between privacy awareness and knowledge based on information provided by respondents. Most socio-demographic variables did not show significant effects on information privacy concerns. Implications of the findings are discussed. Further research is needed to investigate individual concerns on specific information that is being collected, stored, and shared on popular social networking sites.

[Purpose/Significance] In the era of big data, people are flooded with massive information, and problems such as knowledge anxiety, adjustment of resource demand structure, and desire for high-quality information follow one after another. From the perspective of technical support, user interaction is rarely explored, but it is indispensable. The application in the field of infrastructure construction and business is relatively complete, but the user interaction of the knowledge service system is still insufficient: at the theoretical research level, there is a lack of summary of theoretical methods and systematic framework design; at the application practice level, there is a lack of systematic guidance. [Method/Process] The agricultural professional knowledge service system has relatively complete and representative user interaction, a large user base, and a high degree of retention, which is worthy of study, but it has certain shortcomings. The research on user interaction of agricultural knowledge system in this paper is mainly divided into the following three aspects. First, by sorting out the research status of user interaction at home and abroad, the user interaction framework of knowledge service system, namely human-computer interaction and interpersonal interaction, constitutes the basic research framework of this research. Second, based on this, using questionnaires and Baidu statistics this paper investigates the user demand of the agricultural professional knowledge service system, and at the same time analyzes the current situation and deficiencies of the system's supply resources, technologies and service layers. Third, this paper proposes an agricultural professional knowledge service system. The user interaction optimization plan starts from the human-computer interaction and interpersonal interaction dimensions of user interaction, analyzes and optimizes the resources, technologies and service layers of the agricultural knowledge system, realizes the friendly interaction of the system, improves the interaction incentive system, and builds a strong interactive knowledge chain community. [Results/Conclusions] The user interaction frame-work of the knowledge service system is designed, and based on this, we analyzed the current situation of user interaction in the agricultural knowledge system, and realized system optimization. The system can better stimulate user needs and understand user needs for the agricultural knowledge system, innovate functions, and provide high-quality personalized services, maintain the attractiveness and participation stickiness of users, benefit more user groups, and play a guiding role in the realization of system upgrades. Due to the lack of relevant knowledge of algorithm technology and lack of technical design for the optimization of agricultural professional knowledge service system, we need to explore the technical layer in the follow-up research; after the system optimization plan is proposed and implemented, experts and scholars need to further test its improvement effect, and propose construction to better realize the all-round optimization of the user interaction of the agricultural knowledge system.

A data intermediary acquires signals from individual consumers regarding their preferences. The intermediary resells the information in a product market wherein firms and consumers tailor their choices to the demand data. The social dimension of the individual data—whereby a consumer's data are predictive of others' behavior—generates a data externality that can reduce the intermediary's cost of acquiring the information. The intermediary optimally preserves the privacy of consumers' identities if and only if doing so increases social surplus. This policy enables the intermediary to capture the total value of the information as the number of consumers becomes large.

Winner, Association of American Publishers' Professional and Scholarly Publishing Awards in Communication and Cultural Studies Taking English culture as its representative sample, The Secret History of Domesticity asks how the modern notion of the public-private relation emerged in the seventeenth and eighteenth centuries. Treating that relation as a crucial instance of the modern division of knowledge, Michael McKeon narrates its pre-history along with that of its essential component, domesticity. This narrative draws upon the entire spectrum of English people's experience. At the most \"public\" extreme are political developments like the formation of civil society over against the state, the rise of contractual thinking, and the devolution of absolutism from monarch to individual subject. The middle range of experience takes in the influence of Protestant and scientific thought, the printed publication of the private, the conceptualization of virtual publics—society, public opinion, the market—and the capitalization of production, the decline of the domestic economy, and the increase in the sexual division of labor. The most \"private\" pole of experience involves the privatization of marriage, the family, and the household, and the complex entanglement of femininity, interiority, subjectivity, and sexuality. McKeon accounts for how the relationship between public and private experience first became intelligible as a variable interaction of distinct modes of being—not a static dichotomy, but a tool to think with. Richly illustrated with nearly 100 images, including paintings, engravings, woodcuts, and a representative selection of architectural floor plans for domestic interiors, this volume reads graphic forms to emphasize how susceptible the public-private relation was to concrete and spatial representation. McKeon is similarly attentive to how literary forms evoked a tangible sense of public-private relations—among them figurative imagery, allegorical narration, parody, the author-character-reader dialectic, aesthetic distance, and free indirect discourse. He also finds a structural analogue for the emergence of the modern public-private relation in the conjunction of what contemporaries called the \"secret history\" and the domestic novel. A capacious and synthetic historical investigation, The Secret History of Domesticity exemplifies how the methods of literary interpretation and historical analysis can inform and enrich one another.

Mobility trajectory data is of great significance for mobility pattern study, urban computing, and city science. Self-driving, traffic prediction, environment estimation, and many other applications require large-scale mobility trajectory datasets. However, mobility trajectory data acquisition is challenging due to privacy concerns, commercial considerations, missing values, and expensive deployment costs. Nowadays, mobility trajectory data generation has become an emerging trend in reducing the difficulty of mobility trajectory data acquisition by generating principled data. Despite the popularity of mobility trajectory data generation, literature surveys on this topic are rare. In this paper, we present a survey for mobility trajectory generation by artificial intelligence from knowledge-driven and data-driven views. Specifically, we will give a taxonomy of the literature of mobility trajectory data generation, examine mainstream theories and techniques as well as application scenarios for generating mobility trajectory data, and discuss some critical challenges facing this area.