Your Apps Give You Away

Abstract

Understanding mobile app usage has become instrumental to service providers to optimize their online services. Meanwhile, there is a growing privacy concern that users' app usage may uniquely reveal who they are. In this paper, we seek to understand how likely a user can be uniquely re-identified in the crowd by the apps she uses. We systematically quantify the uniqueness of app usage via large-scale empirical measurements. By collaborating with a major cellular network provider, we obtained a city-scale anonymized dataset on mobile app traffic (1.37 million users, 2000 apps, 9.4 billion network connection records). Through extensive analysis, we show that the set of apps that a user has installed is already highly unique. For users with more than 10 apps, 88% of them can be uniquely re-identified by 4 random apps. The uniqueness level is even higher if we consider when and where the apps are used. We also observe that user attributes (e.g., gender, social activity, and mobility patterns) all have an impact on the uniqueness of app usage. Our work takes the first step towards understanding the unique app usage patterns for a large user population, paving the way for further research to develop privacy-protection techniques and building personalized online services.

Behavioral consistency in the digital age

“…Perhaps this should come as no surprise. It is consistent with other research that shows how simple meta-data can be used to make inferences about a particular user, such as personality from smartphone operating system used (Shaw et al, 2016), a particular user from installed apps (Tu et al, 2018) and a person's home location from sparse call logs (Mayer, Mutchler & Mitchell, 2016). Given that many websites and apps collect this metadata from their users, it is important to acknowledge that usage alone can be sufficient to identify a user if misused.…”

”What Apps Did You Use?”: Understanding the Long-term Evolution of Mobile App Usage

“…Unlike previous works whose mobile app usage datasets are collected only from one city [8,23,32] or one country [31], our users are distributed across the world. The long-term users are from 87 countries.…”

“…In recent years, countless efforts have been made to study mobile app usage. Existing studies principally explore users' static behavior based on short-term datasets collected in a given time window ranging from one week [5,23,26], several months [6,14,15,27,32], and up to one year [18]. However, existing research falls short in studying the long-term evolution of users' app usage since they are limited by the short time span of their datasets.…”

Big-Data-Based Power Battery Recycling for New Energy Vehicles: Information Sharing Platform and Intelligent Transportation Optimization

“…Based on the typical input-output logic from decommissioning battery to recycling, according to the pipeline theory, the power battery recycling platform of intelligent big data is used to connect the waste production unit, comprehensive utilization enterprise, logistics enterprise, storage unit and regulatory department, and five technologies of cloud computing, IOT, Internet of vehicles (IOV), Internet and big data are integrated into the power battery recycling field, which is expected to be realized for information sharing, resource docking, process design planning, process tracking feedback, business integration optimization and decision-making intelligence. Zhang et al [96]proposed an efficient approach to build a multi-dimensional index for the Cloud computing system,which can process typical multi-dimensional queries including point queries and range queries efficiently.The uniqueness of APP usage via large-scale empirical measurements was systematically quantified by Tu et al [97],and by collaborating with a major cellular network provider, they obtained a city-scale anonymized dataset on mobile app traffic.Guo et al [98]first proposed a crowd-powered event model and a generic event storyline generation framework based on which a multi-clue-based approach to fine-grained event summarization is presented.The operation mechanism of the power battery recovery platform based on big data is shown in Fig.2.…”