You are your Metadata: Identification and Obfuscation of Social Media Users using Metadata Information

Metadata are associated to most of the information we produce in our daily interactions and communication in the digital world. Yet, surprisingly, metadata are often still catergorized as non-sensitive. Indeed, in the past, researchers and practitioners have mainly focused on the problem of the identification of a user from the content of a message. In this paper, we use Twitter as a case study to quantify the uniqueness of the association between metadata and user identity and to understand the effectiveness of potential obfuscation strategies. More specifically, we analyze atomic fields in the metadata and systematically combine them in an effort to classify new tweets as belonging to an account using different machine learning algorithms of increasing complexity. We demonstrate that through the application of a supervised learning algorithm, we are able to identify any user in a group of 10,000 with approximately 96.7% accuracy. Moreover, if we broaden the scope of our search and consider the 10 most likely candidates we increase the accuracy of the model to 99.22%. We also found that data obfuscation is hard and ineffective for this type of data: even after perturbing 60% of the training data, it is still possible to classify users with an accuracy higher than 95%. These results have strong implications in terms of the design of metadata obfuscation strategies, for example for data set release, not only for Twitter, but, more generally, for most social media platforms.Comment: 11 pages, 13 figures. Published in the Proceedings of the 12th International AAAI Conference on Web and Social Media (ICWSM 2018). June 2018. Stanford, CA, US

Why are managers happier than workers?

This paper studies the determinants of differences in self reported job satisfaction across occupations using data from the 2006/2007 European Social Survey (18 countries). When the effect of other variables is not accounted for, being a Manager yields a "satisfaction bonus" two times as big as the one provided by Workers positions. This substantial satisfaction gap between those holding Managerial positions and Workers practically disappears when we control for individual, household and work related variables. Even though the differences across occupations are reduced, all occupations bring about more job satisfaction than manual and service positions. All results hold when using the European Working Conditions Survey data set. In addition, the results are robust to the use of job satisfaction as a categorical variable and to a variation of the model specification that takes into account the potential endogeneity of the occupational choice.JRC.DG.G.9-Econometrics and applied statistic

Machine learning techniques for identification using mobile and social media data

Networked access and mobile devices provide near constant data generation and collection. Users, environments, applications, each generate different types of data; from the voluntarily provided data posted in social networks to data collected by sensors on mobile devices, it is becoming trivial to access big data caches. Processing sufficiently large amounts of data results in inferences that can be characterized as privacy invasive. In order to address privacy risks we must understand the limits of the data exploring relationships between variables and how the user is reflected in them. In this dissertation we look at data collected from social networks and sensors to identify some aspect of the user or their surroundings. In particular, we find that from social media metadata we identify individual user accounts and from the magnetic field readings we identify both the (unique) cellphone device owned by the user and their course-grained location. In each project we collect real-world datasets and apply supervised learning techniques, particularly multi-class classification algorithms to test our hypotheses. We use both leave-one-out cross validation as well as k-fold cross validation to reduce any bias in the results. Throughout the dissertation we find that unprotected data reveals sensitive information about users. Each chapter also contains a discussion about possible obfuscation techniques or countermeasures and their effectiveness with regards to the conclusions we present. Overall our results show that deriving information about users is attainable and, with each of these results, users would have limited if any indication that any type of analysis was taking place

Strategy and Organisational Cybersecurity: A Knowledge-Problem Perspective

Purpose: The purpose of this paper is to frame organisational cybersecurity through a strategic lens, as a function of an interplay of pragmatism, inference, holism and adaptation. The authors address the hostile epistemic climate for intellectual capital management presented by the dynamics of cybersecurity as a phenomenon. The drivers of this hostility are identified and their implications for research and practice are discussed. Design/methodology/approach: The philosophical foundations of cybersecurity in its relation with strategy, knowledge and intellectual capital are explored through a review of the literature as a mechanism to contribute to the emerging theoretical underpinnings of the cybersecurity domain. Findings: This conceptual paper argues that a knowledge-based perspective can serve as the necessary platform for a phenomenon-based view of organisational cybersecurity, given its multi-disciplinary nature. Research limitations/implications: By recognising the knowledge-related vectors, mechanisms and tendencies at play, a novel perspective on the topic can be developed: cybersecurity as a “knowledge problem”. In order to facilitate such a perspective, the paper proposes an emergent epistemology, rooted in systems thinking and pragmatism. Practical implications: In practice, the knowledge-problem narrative can underpin the development of new organisational support constructs and systems. These can address the distinctiveness of the strategic challenges that cybersecurity poses for the growing operational reliance on intellectual capital. Originality/value: The research narrative presents a novel knowledge-based analysis of organisational cybersecurity, with significant implications for both interdisciplinary research in the field, and practice

Detecting the Presence of Electronic Devices in Smart Homes Using Harmonic Radar

Data about users is collected constantly by phones, cameras, Internet websites, and others. The advent of so-called ‘Smart Things\u27 now enable ever-more sensitive data to be collected inside that most private of spaces: the home. The first step in helping users regain control of their information (inside their home) is to alert them to the presence of potentially unwanted electronics. In this paper, we present a system that could help homeowners (or home dwellers) find electronic devices in their living space. Specifically, we demonstrate the use of harmonic radars (sometimes called nonlinear junction detectors), which have also been used in applications ranging from explosives detection to insect tracking. We adapt this radar technology to detect consumer electronics in a home setting and show that we can indeed accurately detect the presence of even ‘simple’ electronic devices like a smart lightbulb. We evaluate the performance of our radar in both wired and over-the-air transmission scenarios

Are social enterprises technological innovative?:A quantitative analysis on social entrepreneurs in emerging countries

Technological innovation is the new backbone for companies. Exploiting and exploring new knowledge increase the chance of survival in the current dynamic market. Alongside, there are countries were be an innovative need to face up social and political challenges. This has transformed their economy, spreading an entrepreneurial mindset mingled with the willing to help a local community. This phenomenon is called social entrepreneurship which is leveraging new economies and building wealth, environmental system. In this vein, the present research seeks to offer qualitative research on 142 social entrepreneurs in an emerging country. The scope is to analyse if social entrepreneurship, entrepreneurial characteristics, and entrepreneurial ecosystem influence innovation. As emerged, technological innovation is affected by the first two factors but the entrepreneurial ecosystem is still not supportive. New, several activities should be organised by the government to assist entrepreneurs, whereas, the entrepreneurs are socially motivated to build up his enterprise.</p

I call BS: Fraud Detection in Crowdfunding Campaigns

Donations to charity-based crowdfunding environments have been on the rise in the last few years. Unsurprisingly, deception and fraud in such platforms have also increased, but have not been thoroughly studied to understand what characteristics can expose such behavior and allow its automatic detection and blocking. Indeed, crowdfunding platforms are the only ones typically performing oversight for the campaigns launched in each service. However, they are not properly incentivized to combat fraud among users and the campaigns they launch: on the one hand, a platform's revenue is directly proportional to the number of transactions performed (since the platform charges a fixed amount per donation); on the other hand, if a platform is transparent with respect to how much fraud it has, it may discourage potential donors from participating. In this paper, we take the first step in studying fraud in crowdfunding campaigns. We analyze data collected from different crowdfunding platforms, and annotate 700 campaigns as fraud or not. We compute various textual and image-based features and study their distributions and how they associate with campaign fraud. Using these attributes, we build machine learning classifiers, and show that it is possible to automatically classify such fraudulent behavior with up to 90.14% accuracy and 96.01% AUC, only using features available from the campaign's description at the moment of publication (i.e., with no user or money activity), making our method applicable for real-time operation on a user browser

Bax regulates neuronal Ca2+ homeostasis

Excessive Ca(2+) entry during glutamate receptor overactivation (\u22excitotoxicity\u22) induces acute or delayed neuronal death. We report here that deficiency in bax exerted broad neuroprotection against excitotoxic injury and oxygen/glucose deprivation in mouse neocortical neuron cultures and reduced infarct size, necrotic injury, and cerebral edema formation after middle cerebral artery occlusion in mice. Neuronal Ca(2+) and mitochondrial membrane potential (Δψm) analysis during excitotoxic injury revealed that bax-deficient neurons showed significantly reduced Ca(2+) transients during the NMDA excitation period and did not exhibit the deregulation of Δψm that was observed in their wild-type (WT) counterparts. Reintroduction of bax or a bax mutant incapable of proapoptotic oligomerization equally restored neuronal Ca(2+) dynamics during NMDA excitation, suggesting that Bax controlled Ca(2+) signaling independently of its role in apoptosis execution. Quantitative confocal imaging of intracellular ATP or mitochondrial Ca(2+) levels using FRET-based sensors indicated that the effects of bax deficiency on Ca(2+) handling were not due to enhanced cellular bioenergetics or increased Ca(2+) uptake into mitochondria. We also observed that mitochondria isolated from WT or bax-deficient cells similarly underwent Ca(2+)-induced permeability transition. However, when Ca(2+) uptake into the sarco/endoplasmic reticulum was blocked with the Ca(2+)-ATPase inhibitor thapsigargin, bax-deficient neurons showed strongly elevated cytosolic Ca(2+) levels during NMDA excitation, suggesting that the ability of Bax to support dynamic ER Ca(2+) handling is critical for cell death signaling during periods of neuronal overexcitation

Evolutionary trait-based approaches for predicting future global impacts of plant pathogens in the genus Phytophthora

1. Plant pathogens are introduced to new geographical regions ever more frequently as global connectivity increases. Predicting the threat they pose to plant health can be difficult without in‐depth knowledge of behaviour, distribution and spread. Here, we evaluate the potential for using biological traits and phylogeny to predict global threats from emerging pathogens. 2. We use a species‐level trait database and phylogeny for 179 Phytophthora species: oomycete pathogens impacting natural, agricultural, horticultural and forestry settings. We compile host and distribution reports for Phytophthora species across 178 countries and evaluate the power of traits, phylogeny and time since description (reflecting species‐level knowledge) to explain and predict their international transport, maximum latitude and host breadth using Bayesian phylogenetic generalised linear mixed models. 3. In the best‐performing models, traits, phylogeny and time since description together explained up to 90%, 97% and 87% of variance in number of countries reached, latitudinal limits and host range, respectively. Traits and phylogeny together explained up to 26%, 41% and 34% of variance in the number of countries reached, maximum latitude and host plant families affected, respectively, but time since description had the strongest effect. 4. Root‐attacking species were reported in more countries, and on more host plant families than foliar‐attacking species. Host generalist pathogens had thicker‐walled resting structures (stress‐tolerant oospores) and faster growth rates at their optima. Cold‐tolerant species are reported in more countries and at higher latitudes, though more accurate interspecific empirical data are needed to confirm this finding. 5. Policy implications. We evaluate the potential of an evolutionary trait‐based framework to support horizon‐scanning approaches for identifying pathogens with greater potential for global‐scale impacts. Potential future threats from Phytophthora include Phytophthora x heterohybrida, P. lactucae, P. glovera, P. x incrassata, P. amnicola and P. aquimorbida, which are recently described, possibly under‐reported species, with similar traits and/or phylogenetic proximity to other high‐impact species. Priority traits to measure for emerging species may be thermal minima, oospore wall index and growth rate at optimum temperature. Trait‐based horizon‐scanning approaches would benefit from the development of international and cross‐sectoral collaborations to deliver centralised databases incorporating pathogen distributions, traits and phylogeny

Are Zinc-Finger Domains of Protein Kinase C Dynamic Structures That Unfold by Lipid or Redox Activation?

Protein kinase C (PKC) is activated by lipid second messengers or redox action, raising the question whether these activation modes involve the same or alternate mechanisms. Here we show that both lipid activators and oxidation target the zinc-finger domains of PKC, suggesting a unifying activation mechanism. We found that lipid agonist-binding or redox action leads to zinc release and disassembly of zinc fingers, thus triggering large-scale unfolding that underlies conversion to the active enzyme. These results suggest that PKC zinc fingers, originally considered purely structural devices, are in fact redox-sensitive flexible hinges, whose conformation is controlled both by redox conditions and lipid agonists. Antioxid. Redox Signal. 14, 757-766.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/90473/1/ars-2E2010-2E3773.pd