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Urban computing is an exciting area of research with huge amount of urban data being generated every day. Citizens however put their privacy at stake, while generously trying to share data with the society. Nowadays, smart phone is the most common and convenient method of data capture, which has given rise to an emerging paradigm called Mobile Crowdsensing. This work discusses the features of mobile crowdsensing and focuses on the privacy issues in this method of data capture. The work studies privacy threats arising from different sensors and analyses the requirements for privacy. The main contribution of this paper is detailed analysis of how different sensors in the smartphone can unknowingly compromise the user's privacy and reveal his/her lifestyle and routine activities. General TermsPrivacy

Comparisons of the variation of atmospheric radio noise intensities for 20–24 hr to sunspot numbers have been completed. Statistical dependence between the noise intensities and sunspot numbers was found for different seasons at a number of frequencies for many locations in the global network of ARN‐2 noise recorders. The noise intensities generally tended to decrease with sunspot number in the range from 50 kHz to 5 MHz, which is presumed to be due to increases in residual ionospheric absorption during nighttime. At frequencies greater than 5 MHz, noise intensities increased with sunspot number in many cases, which would be expected from our present knowledge of ionospheric behavior in the HF range. By convention, CCIR treats year‐to‐year variation in the noise intensities as random and includes them in the prediction uncertainty σ Fam (for which one value is given at a frequency for a seasonal time block for all locations) in system performance evaluation. An error analysis on a global basis shows that a large portion of the year‐to‐year variability is due to sunspot variation. This suggests the possibility of improved noise estimates.