Reality-Mining with Smartphones: Detecting and Predicting Life Events based on App Installation Behavior

Life events are often described as major forces that are going to shape tomorrow\u27s consumer need, behavior and mood. Thus, the prediction of life events is highly relevant in marketing and sociology. In this paper, we propose a data-driven, real-time method to predict individual life events, using readily available data from smartphones. Our large-scale user study with more than 2000 users shows that our method is able to predict life events with 64.5% higher accuracy, 183.1% better precision and 88.0% higher specificity than a random model on average

Collaborative Filtering on the Blockchain: A Secure Recommender System for e-Commerce

In collaborative filtering approaches, recommendations are inferred from user data. A large volume and a high data quality is essential for an accurate and precise recommender system. As consequence, companies are collecting large amounts of personal user data. Such data is often highly sensitive and ignoring users’ privacy concerns is no option. Companies address these concerns with several risk reduction strategies, but none of them is able to guarantee cryptographic secureness. To close that gap, the present paper proposes a novel recommender system using the advantages of blockchain-supported secure multiparty computation. A potential costumer is able to allow a company to apply a recommendation algorithm without disclosing her personal data. Expected benefits are a reduction of fraud and misuse and a higher willingness to share personal data. An outlined experiment will compare users’ privacy-related behavior in the proposed recommender system with existent solutions

Leave my apps alone!:A study on how Android developers access installed apps on user's device

To enable app interoperability, the Android platform exposes installed application methods (IAMs), i.e., APIs that allow developers to query for the list of apps installed on a user's device. It is known that information collected through IAMs can be used to precisely deduce end-users interests and personal traits, thus raising privacy concerns. In this paper, we present a large-scale empirical study investigating the presence of IAMs in Android apps and their usage by Android developers. Our results highlight that: (i) IAMs are widely used in commercial applications while their popularity is limited in open-source ones; (ii) IAM calls are mostly performed in included libraries code; (iii) more than one-third of libraries that employ IAMs are advertisement libraries; (iv) a small number of popular advertisement libraries account for over 33% of all usages of IAMs by bundled libraries; (v) developers are not always aware that their apps include IAMs calls. Based on the collected data, we confirm the need to (i) revise the way IAMs are currently managed by the Android platform, introducing either an ad-hoc permission or an opt-out mechanism and (ii) improve both developers and end-users awareness with respect to the privacy-related concerns raised by IAMs

Latent class analysis reveals clinically relevant atopy phenotypes in 2 birth cohorts

Background: Phenotypes of childhood-onset asthma are characterized by distinct trajectories and functional features. For atopy, definition of phenotypes during childhood is less clear. Objective: We sought to define phenotypes of atopic sensitization over the first 6 years of life using a latent class analysis (LCA) integrating 3 dimensions of atopy: allergen specificity, time course, and levels of specific IgE (sIgE). Methods: Phenotypes were defined by means of LCA in 680 children of the Multizentrische Allergiestudie (MAS) and 766 children of the Protection against allergy: Study in Rural Environments (PASTURE) birth cohorts and compared with classical nondisjunctive definitions of seasonal, perennial, and food sensitization with respect to atopic diseases and lung function. Cytokine levels were measured in the PASTURE cohort. Results: The LCA classified predominantly by type and multiplicity of sensitization (food vs inhalant), allergen combinations, and sIgE levels. Latent classes were related to atopic disease manifestations with higher sensitivity and specificity than the classical definitions. LCA detected consistently in both cohorts a distinct group of children with severe atopy characterized by high seasonal sIgE levels and a strong propensity for asthma; hay fever; eczema; and impaired lung function, also in children without an established asthma diagnosis. Severe atopy was associated with an increased IL-5/IFN-gamma ratio. A path analysis among sensitized children revealed that among all features of severe atopy, only excessive sIgE production early in life affected asthma risk. Conclusions: LCA revealed a set of benign, symptomatic, and severe atopy phenotypes. The severe phenotype emerged as a latent condition with signs of a dysbalanced immune response. It determined high asthma risk through excessive sIgE production and directly affected impaired lung function.Peer reviewe

Towards Understanding the Impact of Personality Traits on Mobile App Adoption - A Scalable Approach

Smartphones are the most personal devices. The kind of apps we install are therefore closely linked to our habits and personality. In this research-in-progress paper, we aim to provide two key contributions. First, we aim to advance the body of knowledge in technology adoption research by explaining adoption of specific mobile apps by using the Big Five personality traits. Second, we provide a scalable method of deriving personality traits based on easily accessible data. We show that it is possible to determine a user\u27s personality in reasonable accuracy by evaluating her history of app installations and update events