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Register a new userYou Cant Always Get What You Want: The Impact of Prior Assumptions on Interpreting GW190412
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GW190412 is the first observation of a black hole binary with definitively unequal masses. GW190412s mass asymmetry, along with the measured positive effective inspiral spin, allowed for inference of a component black hole spin: the primary black hole in the system was found to have a dimensionless spin magnitude between 0.17 and 0.59 (90% credible range). We investigate how the choice of priors for the spin magnitudes and tilts of the component black holes affect the robustness of parameter estimates for GW190412, and report Bayes factors across a suite of prior assumptions. Depending on the waveform family used to describe the signal, we find either marginal to moderate (2:1-6:1) or strong ($gtrsim$ 20:1) support for the primary black hole being spinning compared to cases where only the secondary is allowed to have spin. We show how these choices influence parameter estimates, and find the asymmetric masses and positive effective inspiral spin of GW190412 to be qualitatively, but not quantitatively, robust to prior assumptions. Our results highlight the importance of both considering astrophysically motivated or population-based priors in interpreting observations and considering their relative support from the data.
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Mobile applications (hereafter, apps) collect a plethora of information regarding the user behavior and his device through third-party analytics libraries. However, the collection and usage of such data raised several privacy concerns, mainly because the end-user - i.e., the actual owner of the data - is out of the loop in this collection process. Also, the existing privacy-enhanced solutions that emerged in the last years follow an all or nothing approach, leaving the user the sole option to accept or completely deny the access to privacy-related data. This work has the two-fold objective of assessing the privacy implications on the usage of analytics libraries in mobile apps and proposing a data anonymization methodology that enables a trade-off between the utility and privacy of the collected data and gives the user complete control over the sharing process. To achieve that, we present an empirical privacy assessment on the analytics libraries contained in the 4500 most-used Android apps of the Google Play Store between November 2020 and January 2021. Then, we propose an empowered anonymization methodology, based on MobHide, that gives the end-user complete control over the collection and anonymization process. Finally, we empirically demonstrate the applicability and effectiveness of such anonymization methodology thanks to HideDroid, a fully-fledged anonymization app for the Android ecosystem.
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