Future consumer mobile phone security: a case study using the data centric security model

In the interconnected world that we live in, traditional security barriers are\ud broken down. Developments such as outsourcing, increased usage of mobile\ud devices and wireless networks each cause new security problems.\ud To address the new security threats, a number of solutions have been suggested,\ud mostly aiming at securing data rather than whole systems or networks.\ud However, these visions (such as proposed by the Jericho Forum [9] and IBM\ud [4]) are mostly concerned with large (inter-) enterprise systems. Until now, it is\ud unclear what data-centric security could mean for other systems and environments.\ud One particular category of systems that has been neglected is that of\ud consumer mobile phones. Currently, data security is usually limited to a PIN\ud number on startup and the option to disable wireless connections. The lack of\ud protection does not seem justified, as these devices have steadily increased in\ud capabilities and capacity; they can connect wirelessly to the Internet and have\ud a high risk of being lost or stolen [8]. This not only puts end users at risk, but\ud also their contacts, as phones can contain privacy sensitive data of many others.\ud For example, if birth dates and addresses are kept with the contact records, in\ud many cases a thief will have enough information to impersonate a contact and\ud steal his identity.\ud Could consumer mobile phones benefit from data-centric security? How\ud useful is data-centric security in this context? These are the core questions we\ud will try to address here

Big Brother is Listening to You: Digital Eavesdropping in the Advertising Industry

In the Digital Age, information is more accessible than ever. Unfortunately, that accessibility has come at the expense of privacy. Now, more and more personal information is in the hands of corporations and governments, for uses not known to the average consumer. Although these entities have long been able to keep tabs on individuals, with the advent of virtual assistants and “always-listening” technologies, the ease by which a third party may extract information from a consumer has only increased. The stark reality is that lawmakers have left the American public behind. While other countries have enacted consumer privacy protections, the United States has no satisfactory legal framework in place to curb data collection by greedy businesses or to regulate how those companies may use and protect consumer data. This Article contemplates one use of that data: digital advertising. Inspired by stories of suspiciously well-targeted advertisements appearing on social media websites, this Article additionally questions whether companies have been honest about their collection of audio data. To address the potential harms consumers may suffer as a result of this deficient privacy protection, this Article proposes a framework wherein companies must acquire users\u27 consent and the government must ensure that businesses do not use consumer information for harmful purposes

Why do People Adopt, or Reject, Smartphone Security Tools?

A large variety of security tools exist for Smartphones, to help their owners to secure the phones and prevent unauthorised others from accessing their data and services. These range from screen locks to antivirus software to password managers. Yet many Smartphone owners do not use these tools despite their being free and easy to use. We were interested in exploring this apparent anomaly. A number of researchers have applied existing models of behaviour from other disciplines to try to understand these kinds of behaviours in a security context, and a great deal of research has examined adoption of screen locking mechanisms. We review the proposed models and consider how they might fail to describe adoption behaviours. We then present the Integrated Model of Behaviour Prediction (IMBP), a richer model than the ones tested thus far. We consider the kinds of factors that could be incorporated into this model in order to understand Smartphone owner adoption, or rejection, of security tools. The model seems promising, based on existing literature, and we plan to test its efficacy in future studies