C3P: Context-Aware Crowdsourced Cloud Privacy

Due to the abundance of attractive services available on the cloud, people are placing an increasing amount of their data online on different cloud platforms. However, given the recent large-scale attacks on users data, privacy has become an important issue. Ordinary users cannot be expected to manually specify which of their data is sensitive or to take appropriate measures to protect such data. Furthermore, usually most people are not aware of the privacy risk that different shared data items can pose. In this paper, we present a novel conceptual framework in which privacy risk is automatically calculated using the sharing context of data items. To overcome ignorance of privacy risk on the part of most users, we use a crowdsourcing based approach. We use Item Response Theory (IRT) on top of this crowdsourced data to determine privacy risk of items and diverse attitudes of users towards privacy. First, we determine the feasibility of IRT for the cloud scenario by asking workers feedback on Amazon mTurk on various sharing scenarios. We obtain a good fit of the responses with the theory, and thus show that IRT, a well-known psychometric model for educational purposes, can be applied to the cloud scenario. Then, we present a lightweight mechanism such that users can crowdsource their sharing contexts with the server and obtain the risk of sharing particular data item(s) anonymously. Finally, we use the Enron dataset to simulate our conceptual framework, and also provide experimental results using synthetic data. We show that our scheme converges quickly and provides accurate privacy risk scores under varying conditions

Search for leptophobic Z ' bosons decaying into four-lepton final states in proton-proton collisions at root s=8 TeV

Peer reviewe

Search for black holes and other new phenomena in high-multiplicity final states in proton-proton collisions at root s=13 TeV

Peer reviewe

Search for high-mass diphoton resonances in proton-proton collisions at 13 TeV and combination with 8 TeV search

Peer reviewe

Search for heavy resonances decaying into a vector boson and a Higgs boson in final states with charged leptons, neutrinos, and b quarks

Peer reviewe

Measurements of differential production cross sections for a Z boson in association with jets in pp collisions at root s=8 TeV

Peer reviewe

Measurement of the mass difference between top quark and antiquark in pp collisions at root s=8 TeV

Peer reviewe

Performance of reconstruction and identification of τ leptons decaying to hadrons and vτ in pp collisions at √s=13 TeV

The algorithm developed by the CMS Collaboration to reconstruct and identify τ leptons produced in proton-proton collisions at √s=7 and 8 TeV, via their decays to hadrons and a neutrino, has been significantly improved. The changes include a revised reconstruction of π⁰ candidates, and improvements in multivariate discriminants to separate τ leptons from jets and electrons. The algorithm is extended to reconstruct τ leptons in highly Lorentz-boosted pair production, and in the high-level trigger. The performance of the algorithm is studied using proton-proton collisions recorded during 2016 at √s=13 TeV, corresponding to an integrated luminosity of 35.9 fb¯¹. The performance is evaluated in terms of the efficiency for a genuine τ lepton to pass the identification criteria and of the probabilities for jets, electrons, and muons to be misidentified as τ leptons. The results are found to be very close to those expected from Monte Carlo simulation