Undermining User Privacy on Mobile Devices Using AI

Over the past years, literature has shown that attacks exploiting the microarchitecture of modern processors pose a serious threat to the privacy of mobile phone users. This is because applications leave distinct footprints in the processor, which can be used by malware to infer user activities. In this work, we show that these inference attacks are considerably more practical when combined with advanced AI techniques. In particular, we focus on profiling the activity in the last-level cache (LLC) of ARM processors. We employ a simple Prime+Probe based monitoring technique to obtain cache traces, which we classify with Deep Learning methods including Convolutional Neural Networks. We demonstrate our approach on an off-the-shelf Android phone by launching a successful attack from an unprivileged, zeropermission App in well under a minute. The App thereby detects running applications with an accuracy of 98% and reveals opened websites and streaming videos by monitoring the LLC for at most 6 seconds. This is possible, since Deep Learning compensates measurement disturbances stemming from the inherently noisy LLC monitoring and unfavorable cache characteristics such as random line replacement policies. In summary, our results show that thanks to advanced AI techniques, inference attacks are becoming alarmingly easy to implement and execute in practice. This once more calls for countermeasures that confine microarchitectural leakage and protect mobile phone applications, especially those valuing the privacy of their users

The future of Cybersecurity in Italy: Strategic focus area

This volume has been created as a continuation of the previous one, with the aim of outlining a set of focus areas and actions that the Italian Nation research community considers essential. The book touches many aspects of cyber security, ranging from the definition of the infrastructure and controls needed to organize cyberdefence to the actions and technologies to be developed to be better protected, from the identification of the main technologies to be defended to the proposal of a set of horizontal actions for training, awareness raising, and risk management

A New Compact for Sexual Privacy

Intimate life is under constant surveillance. Firms track people’s periods, hot flashes, abortions, sexual assaults, sex toy use, sexual fantasies, and nude photos. Individuals hardly appreciate the extent of the monitoring, and even if they did, little can be done to curtail it. What is big business for firms is a big risk for individuals. The handling of intimate data undermines the values that sexual privacy secures—autonomy, dignity, intimacy, and equality. It can imperil people’s job, housing, insurance, and other crucial opportunities. More often, women and minorities shoulder a disproportionate amount of the burden. Privacy law is failing us. Our consumer protection approach offers little protection. Not only is the private-sector’s handling of intimate information largely unrestrained, but it is treated as normative. This Article offers a new compact for the protection of intimate information. Fundamental civil rights and liberties, along with consumer protection, is at stake. The new compact seeks to stem the tidal wave of collection, restrict certain uses of intimate data, and expand the suite of remedies available to courts. It draws upon the lessons of civil rights law in moving beyond procedural protections and in authorizing injunctive relief, including orders to stop processing intimate data