Modeling of Personalized Privacy Disclosure Behavior: A Formal Method Approach

In order to create user-centric and personalized privacy management tools, the underlying models must account for individual users' privacy expectations, preferences, and their ability to control their information sharing activities. Existing studies of users' privacy behavior modeling attempt to frame the problem from a request's perspective, which lack the crucial involvement of the information owner, resulting in limited or no control of policy management. Moreover, very few of them take into the consideration the aspect of correctness, explainability, usability, and acceptance of the methodologies for each user of the system. In this paper, we present a methodology to formally model, validate, and verify personalized privacy disclosure behavior based on the analysis of the user's situational decision-making process. We use a model checking tool named UPPAAL to represent users' self-reported privacy disclosure behavior by an extended form of finite state automata (FSA), and perform reachability analysis for the verification of privacy properties through computation tree logic (CTL) formulas. We also describe the practical use cases of the methodology depicting the potential of formal technique towards the design and development of user-centric behavioral modeling. This paper, through extensive amounts of experimental outcomes, contributes several insights to the area of formal methods and user-tailored privacy behavior modeling

A User-Centric and Sentiment Aware Privacy-Disclosure Detection Framework Based on Multi-Input Neural Network

Data and information privacy is a major concern of today’s world. More specifically, users’ digital privacy has become one of the most important issues to deal with, as advancements are being made in information sharing technology. An increasing number of users are sharing information through text messages, emails, and social media without proper awareness of privacy threats and their consequences. One approach to prevent the disclosure of private information is to identify them in a conversation and warn the dispatcher before the conveyance happens between the sender and the receiver. Another way of preventing information (sensitive) loss might be to analyze and sanitize a batch of offline documents when the data is already accumulated somewhere. However, automating the process of identifying user-centric privacy disclosure in textual data is challenging. This is because the natural language has an extremely rich form and structure with different levels of ambiguities. Therefore, we inquire after a potential framework that could bring this challenge within reach by precisely recognizing users’ privacy disclosures in a piece of text by taking into account - the authorship and sentiment (tone) of the content alongside the linguistic features and techniques. The proposed framework is considered as the supporting plugin to help text classification systems more accurately identify text that might disclose the author’s personal or private information

Reduction of lipoxidative load by secretory phospholipase A2 inhibition protects against neurovascular injury following experimental stroke in rat

<p>Abstract</p> <p>Background</p> <p>In animal models, ischemia reperfusion (IR) injury triggers membrane lipid degradation and accumulation of lipoxidative exacerbations in neurovascular unit, leading to blood brain barrier (BBB) damage and neurologic deficits. In this study, we investigated whether impeding membrane lipid breakdown by inhibiting secretory phospholipase A2 (sPLA2) activity reduces BBB leakage, leading to neuroprotection and functional recovery.</p> <p>Methods</p> <p>Focal cerebral IR injury was induced by middle cerebral artery occlusion (MCAO) in adult male rats. A sPLA2 inhibitor, 7,7-dimethyleicosadienoic acid (DEDA), was administered following IR injury. DEDA-treated animals were compared with vehicle-treated in terms of BBB leakage, edema, infarct volume, and neurological deficit. Membrane lipid degradation and the expression/activity of sPLA2 were also assessed. The role of one of the sPLA2 products, arachidonic acid (AA), on the morphology of the differentiated neuronal cell PC12 was examined by light microscopy.</p> <p>Results</p> <p>Treatment with DEDA after IR injury not only reduced BBB leakage but also decreased infarct volume and improved neurologic function. The treatment attenuated both the activity of sPLA2 and the levels of sPLA2-derived oxidized products. The metabolites of lipid oxidation/peroxidation, including the protein carbonyl, were reduced as well. The treatment also restored the levels of glutathione, indicating attenuation of oxidative stress. I<it>n vitro </it>treatment of PC12 cells with DEDA did not restore the AA-mediated inhibition of neurite formation and the levels of glutathione, indicating that effect of DEDA is up stream to AA release.</p> <p>Conclusion</p> <p>sPLA2-derived oxidative products contribute to significant neurovascular damage, and treatment with sPLA2 inhibitor DEDA ameliorates secondary injury by reducing exacerbations from lipoxidative stress.</p