PerfWeb: How to Violate Web Privacy with Hardware Performance Events

The browser history reveals highly sensitive information about users, such as financial status, health conditions, or political views. Private browsing modes and anonymity networks are consequently important tools to preserve the privacy not only of regular users but in particular of whistleblowers and dissidents. Yet, in this work we show how a malicious application can infer opened websites from Google Chrome in Incognito mode and from Tor Browser by exploiting hardware performance events (HPEs). In particular, we analyze the browsers' microarchitectural footprint with the help of advanced Machine Learning techniques: k-th Nearest Neighbors, Decision Trees, Support Vector Machines, and in contrast to previous literature also Convolutional Neural Networks. We profile 40 different websites, 30 of the top Alexa sites and 10 whistleblowing portals, on two machines featuring an Intel and an ARM processor. By monitoring retired instructions, cache accesses, and bus cycles for at most 5 seconds, we manage to classify the selected websites with a success rate of up to 86.3%. The results show that hardware performance events can clearly undermine the privacy of web users. We therefore propose mitigation strategies that impede our attacks and still allow legitimate use of HPEs

Onco-miR-155 targets SHIP1 to promote TNFalpha-dependent growth of B cell lymphomas.

Non-coding microRNAs (miRs) are a vital component of post-transcriptional modulation of protein expression and, like coding mRNAs harbour oncogenic properties. However, the mechanisms governing miR expression and the identity of the affected transcripts remain poorly understood. Here we identify the inositol phosphatase SHIP1 as a bonafide target of the oncogenic miR-155. We demonstrate that in diffuse large B cell lymphoma (DLBCL) elevated levels of miR-155, and consequent diminished SHIP1 expression are the result of autocrine stimulation by the pro-inflammatory cytokine tumour necrosis factor a (TNFalpha). Anti-TNFalpha regimen such as eternacept or infliximab were sufficient to reduce miR-155 levels and restored SHIP1 expression in DLBCL cells with an accompanying reduction in cell proliferation. Furthermore, we observed a substantial decrease in tumour burden in DLBCL xenografts in response to eternacept. These findings strongly support the concept that cytokine-regulated miRs can function as a crucial link between inflammation and cancer, and illustrate the feasibility of anti-TNFalpha therapy as a novel and immediately accessible (co)treatment for DLBCL

Multiplex cytokine analysis of dermal interstitial blister fluid defines local disease mechanisms in systemic sclerosis.

Clinical diversity in systemic sclerosis (SSc) reflects multifaceted pathogenesis and the effect of key growth factors or cytokines operating within a disease-specific microenvironment. Dermal interstitial fluid sampling offers the potential to examine local mechanisms and identify proteins expressed within lesional tissue. We used multiplex cytokine analysis to profile the inflammatory and immune activity in the lesions of SSc patients

Interests Diffusion in Social Networks

Understanding cultural phenomena on Social Networks (SNs) and exploiting the implicit knowledge about their members is attracting the interest of different research communities both from the academic and the business side. The community of complexity science is devoting significant efforts to define laws, models, and theories, which, based on acquired knowledge, are able to predict future observations (e.g. success of a product). In the mean time, the semantic web community aims at engineering a new generation of advanced services by defining constructs, models and methods, adding a semantic layer to SNs. In this context, a leapfrog is expected to come from a hybrid approach merging the disciplines above. Along this line, this work focuses on the propagation of individual interests in social networks. The proposed framework consists of the following main components: a method to gather information about the members of the social networks; methods to perform some semantic analysis of the Domain of Interest; a procedure to infer members' interests; and an interests evolution theory to predict how the interests propagate in the network. As a result, one achieves an analytic tool to measure individual features, such as members' susceptibilities and authorities. Although the approach applies to any type of social network, here it is has been tested against the computer science research community. The DBLP (Digital Bibliography and Library Project) database has been elected as test-case since it provides the most comprehensive list of scientific production in this field.Comment: 30 pages 13 figs 4 table