User Privacy in Mobile Advertising

With the pervasiveness of mobile devices in our daily life continuously increasing, mobile advertising is emerging as an important marketing strategy. However, due to its intrusive nature in practice, there has been a growing concern over users’ privacy in mobile advertising, especially push-based mode, which can affect consumers’ acceptance and effectiveness of mobile advertising. Aiming to gain a deeper understanding of not only users’ concerns of privacy intrusion in mobile advertising, but also the potential solutions to addressing those concerns, we conducted a survey in this study. The findings of this study provide a few useful insights for researchers, advertisers, and businesses on both the importance and methods of privacy protection in mobile advertising from a user perspective

User profile modelling based on mobile phone sensing and call logs

There are remaining questions concerning user profile modelling in the mobile advertising domain. The research question addressed in this paper is how to design a specific user profile model, that is a simplified model in terms of the amount of user data to be collected, that considers relevant aspects of mobile advertising such as social and personal context, and user privacy preservation. To address this question, a new user profile model consisting of three phases was proposed: (1) data collection, (2) integration and normalization of collected data, and (3) inference of knowledge about the mobile user’s profile. The most significant contributions of the proposed model are a simplified user profile model approach which tackles the dependency on other data sources like OSN platforms and local data gathering and storage that contributes to the user privacy-preserving since the user can exert more control over his/her personal data

Towards a User Privacy-Aware Mobile Gaming App Installation Prediction Model

Over the past decade, programmatic advertising has received a great deal of attention in the online advertising industry. A real-time bidding (RTB) system is rapidly becoming the most popular method to buy and sell online advertising impressions. Within the RTB system, demand-side platforms (DSP) aim to spend advertisers' campaign budgets efficiently while maximizing profit, seeking impressions that result in high user responses, such as clicks or installs. In the current study, we investigate the process of predicting a mobile gaming app installation from the point of view of a particular DSP, while paying attention to user privacy, and exploring the trade-off between privacy preservation and model performance. There are multiple levels of potential threats to user privacy, depending on the privacy leaks associated with the data-sharing process, such as data transformation or de-anonymization. To address these concerns, privacy-preserving techniques were proposed, such as cryptographic approaches, for training privacy-aware machine-learning models. However, the ability to train a mobile gaming app installation prediction model without using user-level data, can prevent these threats and protect the users' privacy, even though the model's ability to predict may be impaired. Additionally, current laws might force companies to declare that they are collecting data, and might even give the user the option to opt out of such data collection, which might threaten companies' business models in digital advertising, which are dependent on the collection and use of user-level data. We conclude that privacy-aware models might still preserve significant capabilities, enabling companies to make better decisions, dependent on the privacy-efficacy trade-off utility function of each case.Comment: 11 pages, 3 figure

Geographic differential privacy for mobile crowd coverage maximization

For real-world mobile applications such as location-based advertising and spatial crowdsourcing, a key to success is targeting mobile users that can maximally cover certain locations in a future period. To find an optimal group of users, existing methods often require information about users' mobility history, which may cause privacy breaches. In this paper, we propose a method to maximize mobile crowd's future location coverage under a guaranteed location privacy protection scheme. In our approach, users only need to upload one of their frequently visited locations, and more importantly, the uploaded location is obfuscated using a geographic differential privacy policy. We propose both analytic and practical solutions to this problem. Experiments on real user mobility datasets show that our method significantly outperforms the state-of-the-art geographic differential privacy methods by achieving a higher coverage under the same level of privacy protection

DO THEY REALLY CARE ABOUT TARGETED POLITICAL ADS? INVESTIGATION OF USER PRIVACY CONCERNS AND PREFERENCES

Reliance on targeted political ads has skyrocketed in recent years, leading to negative reactions in media and society. Nonetheless, only few studies investigate user privacy concerns and their role in user acceptance decisions in the context of online political targeting. To fill this gap, in this study we explore the magnitude of privacy concerns towards targeted political ads compared to “tradi-tional” targeting in the product context. Surprisingly, we find no notable differences in privacy concerns between these use purposes. In the next step, user preferences over ad types are elicited with the help of a discrete choice experiment in the mobile app adoption context. Among others, our findings from simulations on the basis of a mixed logit model cautiously suggest that while targeted political advertising is perceived as somewhat less desirable by respondents, its presence does not consequentially deter users from choosing such an app, with user preferences being high-ly volatile. Together, these results contribute to a better understanding of users’ privacy concerns and preferences in the context of targeted political advertising online. Acknowledgment This work has been funded by the Federal Ministry of Education and Research of Germany (BMBF) under grant no. 16DII116 (“Deutsches Internet-Institut”)

Joint optimisation of privacy and cost of in-app mobile user profiling and targeted ads

Online mobile advertising ecosystems provide advertising and analytics services that collect, aggregate, process and trade rich amount of consumer's personal data and carries out interests-based ads targeting, which raised serious privacy risks and growing trends of users feeling uncomfortable while using internet services. In this paper, we address user's privacy concerns by developing an optimal dynamic optimisation cost-effective framework for preserving user privacy for profiling, ads-based inferencing, temporal apps usage behavioral patterns and interest-based ads targeting. A major challenge in solving this dynamic model is the lack of knowledge of time-varying updates during profiling process. We formulate a mixed-integer optimisation problem and develop an equivalent problem to show that proposed algorithm does not require knowledge of time-varying updates in user behavior. Following, we develop an online control algorithm to solve equivalent problem using Lyapunov optimisation and to overcome difficulty of solving nonlinear programming by decomposing it into various cases and achieve trade-off between user privacy, cost and targeted ads. We carry out extensive experimentations and demonstrate proposed framework's applicability by implementing its critical components using POC `System App'. We compare proposed framework with other privacy protecting approaches and investigate that it achieves better privacy and functionality for various performance parameters

Mobile Privacy and Business-to-Platform Dependencies: An Analysis of SEC Disclosures

This Article systematically examines the dependence of mobile apps on mobile platforms for the collection and use of personal information through an analysis of Securities and Exchange Commission (SEC) filings of mobile app companies. The Article uses these disclosures to find systematic evidence of how app business models are shaped by the governance of user data by mobile platforms, in order to reflect on the role of platforms in privacy regulation more generally. The analysis of SEC filings documented in the Article produces new and unique insights into the data practices and data-related aspects of the business models of popular mobile apps and shows the value of SEC filings for privacy law and policy research more generally. The discussion of SEC filings and privacy builds on regulatory developments in SEC disclosures and cybersecurity of the last decade. The Article also connects to recent regulatory developments in the U.S. and Europe, including the General Data Protection Regulation, the proposals for a new ePrivacy Regulation and a Regulation of fairness in business-to-platform relations

Characterizing Location-based Mobile Tracking in Mobile Ad Networks

Mobile apps nowadays are often packaged with third-party ad libraries to monetize user data