Detrimental network effects in privacy: A graph-theoretic model for node-based intrusions.

Despite proportionality being one of the tenets of data protection laws, we currently lack a robust analytical framework to evaluate the reach of modern data collections and the network effects at play. Here, we propose a graph-theoretic model and notions of node- and edge-observability to quantify the reach of networked data collections. We first prove closed-form expressions for our metrics and quantify the impact of the graph's structure on observability. Second, using our model, we quantify how (1) from 270,000 compromised accounts, Cambridge Analytica collected 68.0M Facebook profiles; (2) from surveilling 0.01% of the nodes in a mobile phone network, a law enforcement agency could observe 18.6% of all communications; and (3) an app installed on 1% of smartphones could monitor the location of half of the London population through close proximity tracing. Better quantifying the reach of data collection mechanisms is essential to evaluate their proportionality.

Emergence of network effects and predictability in the judicial system.

As courts strive to simultaneously remain self-consistent and adapt to new legal challenges, a complex network of of citations between decided cases is established. Using network science methods to analyze the underlying patterns of citations between cases can help us understand the large-scale mechanisms which shape the judicial system. Here, we use the case-to-case citation structure of the Court of Justice of the European Union to examine this question. Using a link-prediction model, we show that over time the complex network of citations evolves in a way which improves our ability to predict new citations. Investigating the factors which enable prediction over time, we find that the content of the case documents plays a decreasing role, whereas both the predictive power and significance of the citation network structure itself show a consistent increase over time. Finally, our analysis enables us to validate existing citations and recommend potential citations for future cases within the court.

Inferring transportation mode from smartphone sensors: Evaluating the potential of Wi-Fi and Bluetooth.

Understanding which transportation modes people use is critical for smart cities and planners to better serve their citizens. We show that using information from pervasive Wi-Fi access points and Bluetooth devices can enhance GPS and geographic information to improve transportation detection on smartphones. Wi-Fi information also improves the identification of transportation mode and helps conserve battery since it is already collected by most mobile phones. Our approach uses a machine learning approach to determine the mode from pre-prepocessed data. This approach yields an overall accuracy of 89% and average F1 score of 83% for inferring the three grouped modes of self-powered, car-based, and public transportation. When broken out by individual modes, Wi-Fi features improve detection accuracy of bus trips, train travel, and driving compared to GPS features alone and can substitute for GIS features without decreasing performance. Our results suggest that Wi-Fi and Bluetooth can be useful in urban transportation research, for example by improving mobile travel surveys and urban sensing applications.

Interaction data from the Copenhagen Networks Study.

We describe the multi-layer temporal network which connects a population of more than 700 university students over a period of four weeks. The dataset was collected via smartphones as part of the Copenhagen Networks Study. We include the network of physical proximity among the participants (estimated via Bluetooth signal strength), the network of phone calls (start time, duration, no content), the network of text messages (time of message, no content), and information about Facebook friendships. Thus, we provide multiple types of communication networks expressed in a single, large population with high temporal resolution, and over a period of multiple weeks, a fact which makes the dataset shared here unique. We expect that reuse of this dataset will allow researchers to make progress on the analysis and modeling of human social networks.

Evidence for a conserved quantity in human mobility.

Recent seminal works on human mobility have shown that individuals constantly exploit a small set of repeatedly visited locations1-3. A concurrent study has emphasized the explorative nature of human behaviour, showing that the number of visited places grows steadily over time4-7. How to reconcile these seemingly contradicting facts remains an open question. Here, we analyse high-resolution multi-year traces of ~40,000 individuals from 4 datasets and show that this tension vanishes when the long-term evolution of mobility patterns is considered. We reveal that mobility patterns evolve significantly yet smoothly, and that the number of familiar locations an individual visits at any point is a conserved quantity with a typical size of ~25. We use this finding to improve state-of-the-art modelling of human mobility4,8. Furthermore, shifting the attention from aggregated quantities to individual behaviour, we show that the size of an individual's set of preferred locations correlates with their number of social interactions. This result suggests a connection between the conserved quantity we identify, which as we show cannot be understood purely on the basis of time constraints, and the 'Dunbar number'9,10 describing a cognitive upper limit to an individual's number of social relations. We anticipate that our work will spark further research linking the study of human mobility and the cognitive and behavioural sciences.

The role of gender in social network organization.

The digital traces we leave behind when engaging with the modern world offer an interesting lens through which we study behavioral patterns as expression of gender. Although gender differentiation has been observed in a number of settings, the majority of studies focus on a single data stream in isolation. Here we use a dataset of high resolution data collected using mobile phones, as well as detailed questionnaires, to study gender differences in a large cohort. We consider mobility behavior and individual personality traits among a group of more than 800 university students. We also investigate interactions among them expressed via person-to-person contacts, interactions on online social networks, and telecommunication. Thus, we are able to study the differences between male and female behavior captured through a multitude of channels for a single cohort. We find that while the two genders are similar in a number of aspects, there are robust deviations that include multiple facets of social interactions, suggesting the existence of inherent behavioral differences. Finally, we quantify how aspects of an individual's characteristics and social behavior reveals their gender by posing it as a classification problem. We ask: How well can we distinguish between male and female study participants based on behavior alone? Which behavioral features are most predictive?

Evidence of complex contagion of information in social media: An experiment using Twitter bots.

It has recently become possible to study the dynamics of information diffusion in techno-social systems at scale, due to the emergence of online platforms, such as Twitter, with millions of users. One question that systematically recurs is whether information spreads according to simple or complex dynamics: does each exposure to a piece of information have an independent probability of a user adopting it (simple contagion), or does this probability depend instead on the number of sources of exposure, increasing above some threshold (complex contagion)? Most studies to date are observational and, therefore, unable to disentangle the effects of confounding factors such as social reinforcement, homophily, limited attention, or network community structure. Here we describe a novel controlled experiment that we performed on Twitter using 'social bots' deployed to carry out coordinated attempts at spreading information. We propose two Bayesian statistical models describing simple and complex contagion dynamics, and test the competing hypotheses. We provide experimental evidence that the complex contagion model describes the observed information diffusion behavior more accurately than simple contagion. Future applications of our results include more effective defenses against malicious propaganda campaigns on social media, improved marketing and advertisement strategies, and design of effective network intervention techniques.

Multi-scale spatio-temporal analysis of human mobility.

The recent availability of digital traces generated by phone calls and online logins has significantly increased the scientific understanding of human mobility. Until now, however, limited data resolution and coverage have hindered a coherent description of human displacements across different spatial and temporal scales. Here, we characterise mobility behaviour across several orders of magnitude by analysing ∼850 individuals' digital traces sampled every ∼16 seconds for 25 months with ∼10 meters spatial resolution. We show that the distributions of distances and waiting times between consecutive locations are best described by log-normal and gamma distributions, respectively, and that natural time-scales emerge from the regularity of human mobility. We point out that log-normal distributions also characterise the patterns of discovery of new places, implying that they are not a simple consequence of the routine of modern life.

Inferring Stop-Locations from WiFi.

Human mobility patterns are inherently complex. In terms of understanding these patterns, the process of converting raw data into series of stop-locations and transitions is an important first step which greatly reduces the volume of data, thus simplifying the subsequent analyses. Previous research into the mobility of individuals has focused on inferring 'stop locations' (places of stationarity) from GPS or CDR data, or on detection of state (static/active). In this paper we bridge the gap between the two approaches: we introduce methods for detecting both mobility state and stop-locations. In addition, our methods are based exclusively on WiFi data. We study two months of WiFi data collected every two minutes by a smartphone, and infer stop-locations in the form of labelled time-intervals. For this purpose, we investigate two algorithms, both of which scale to large datasets: a greedy approach to select the most important routers and one which uses a density-based clustering algorithm to detect router fingerprints. We validate our results using participants' GPS data as well as ground truth data collected during a two month period.

Tracking Human Mobility Using WiFi Signals.

We study six months of human mobility data, including WiFi and GPS traces recorded with high temporal resolution, and find that time series of WiFi scans contain a strong latent location signal. In fact, due to inherent stability and low entropy of human mobility, it is possible to assign location to WiFi access points based on a very small number of GPS samples and then use these access points as location beacons. Using just one GPS observation per day per person allows us to estimate the location of, and subsequently use, WiFi access points to account for 80% of mobility across a population. These results reveal a great opportunity for using ubiquitous WiFi routers for high-resolution outdoor positioning, but also significant privacy implications of such side-channel location tracking.

Measuring large-scale social networks with high resolution.

This paper describes the deployment of a large-scale study designed to measure human interactions across a variety of communication channels, with high temporal resolution and spanning multiple years-the Copenhagen Networks Study. Specifically, we collect data on face-to-face interactions, telecommunication, social networks, location, and background information (personality, demographics, health, politics) for a densely connected population of 1000 individuals, using state-of-the-art smartphones as social sensors. Here we provide an overview of the related work and describe the motivation and research agenda driving the study. Additionally, the paper details the data-types measured, and the technical infrastructure in terms of both backend and phone software, as well as an outline of the deployment procedures. We document the participant privacy procedures and their underlying principles. The paper is concluded with early results from data analysis, illustrating the importance of multi-channel high-resolution approach to data collection.