In Things We Trust? Towards trustability in the Internet of Things

This essay discusses the main privacy, security and trustability issues with the Internet of Things

An Instagram is worth a thousand words: An industry panel and audience Q&A

PurposeThe purpose of this paper is to describe the industry panel session hosted by Bond University Library at the Australian Library and Information Association's Information Online 2013 Conference. The panel was held to discuss the use and implications of professional Instagram profiles. The panel included a professional photographer, an internet marketing expert, a social media expert, a librarian and a social media‐savvy student. The inclusion of a range of perspectives from outside the library aimed to provide a holistic approach to the institutional use of Instagram and to provide inspiration. The panel took place on Wednesday 13 February 2013 in Brisbane, Queensland, Australia.Design/methodology/approachThe panel discussion covered three broad topic areas: the popularity of Instagram and listening to your audience. The risks, limitations and disadvantages of using Instagram. Engagement with followers and measuring the value of Instagram. Throughout the panel session live mobile polling was used to gather feedback and responses from the audience in regards to their photo‐sharing practices. Real examples from Instagram profiles were shared to stimulate discussion. The session concluded with a Q&amp;A session from the audience.FindingsThe session was attended by approximately 80 delegates. The results of the mobile polling will be included in the body of the article. Based on feedback from delegates on Twitter and Facebook (which was collated via Storify) the session was perceived as a useful introduction to a professional exploration of Instagram and photo sharing.Practical implicationsThe session was an opportunity for conference delegates to hear about Instagram use from professionals in other fields. Each panel member contributed a unique perspective on the use of Instagram. In particular, the inclusion of a current Bond University student on the panel allowed for a customer's perspective on the library's use of Instagram. This discussion and the feedback gathered from the audience has formed the basis for further evidence‐based research.Originality/valueTo date, few libraries are using Instagram. This discussion on the professional use of Instagram contributes to the body of knowledge about library social media use. It also extends the conversation to include mobile photo sharing, an area which has not been well addressed in the literature. This panel was unique in that it brought together professionals from other environments to reflect on library use of Instagram.</jats:sec

Literature Overview - Privacy in Online Social Networks

In recent years, Online Social Networks (OSNs) have become an important\ud part of daily life for many. Users build explicit networks to represent their\ud social relationships, either existing or new. Users also often upload and share a plethora of information related to their personal lives. The potential privacy risks of such behavior are often underestimated or ignored. For example, users often disclose personal information to a larger audience than intended. Users may even post information about others without their consent. A lack of experience and awareness in users, as well as proper tools and design of the OSNs, perpetuate the situation. This paper aims to provide insight into such privacy issues and looks at OSNs, their associated privacy risks, and existing research into solutions. The final goal is to help identify the research directions for the Kindred Spirits project

Privacy Intelligence: A Survey on Image Sharing on Online Social Networks

Image sharing on online social networks (OSNs) has become an indispensable part of daily social activities, but it has also led to an increased risk of privacy invasion. The recent image leaks from popular OSN services and the abuse of personal photos using advanced algorithms (e.g. DeepFake) have prompted the public to rethink individual privacy needs when sharing images on OSNs. However, OSN image sharing itself is relatively complicated, and systems currently in place to manage privacy in practice are labor-intensive yet fail to provide personalized, accurate and flexible privacy protection. As a result, an more intelligent environment for privacy-friendly OSN image sharing is in demand. To fill the gap, we contribute a systematic survey of 'privacy intelligence' solutions that target modern privacy issues related to OSN image sharing. Specifically, we present a high-level analysis framework based on the entire lifecycle of OSN image sharing to address the various privacy issues and solutions facing this interdisciplinary field. The framework is divided into three main stages: local management, online management and social experience. At each stage, we identify typical sharing-related user behaviors, the privacy issues generated by those behaviors, and review representative intelligent solutions. The resulting analysis describes an intelligent privacy-enhancing chain for closed-loop privacy management. We also discuss the challenges and future directions existing at each stage, as well as in publicly available datasets.Comment: 32 pages, 9 figures. Under revie

Image sharing privacy policy on social networks using A3P

User Image sharing social site maintaining privacy has become a major problem, as demonstrated by a recent wave of publicized incidents where users inadvertently shared personal information. In light of these incidents, the need of tools to help users control access to their shared content is apparent. Toward addressing this need an Adaptive Privacy Policy Prediction (A3P) system to help users compose privacy settings for their images. The solution relies on an image classification framework for image categories which may be associated with similar policies and on a policy prediction algorithm to automatically generate a policy for each newly uploaded image, also according to user’s social features. Image Sharing takes place both among previously established groups of known people or social circles and also increasingly with people outside the users social circles, for purposes of social discovery-to help them identify new peers and learn about peers interests and social surroundings, Sharing images within online content sharing sites, therefore, may quickly lead to unwanted disclosure. The aggregated information can result in unexpected exposure of one’s social environment and lead to abuse of one’s personal information

Collaborative Deep Learning for Recommender Systems

Collaborative filtering (CF) is a successful approach commonly used by many recommender systems. Conventional CF-based methods use the ratings given to items by users as the sole source of information for learning to make recommendation. However, the ratings are often very sparse in many applications, causing CF-based methods to degrade significantly in their recommendation performance. To address this sparsity problem, auxiliary information such as item content information may be utilized. Collaborative topic regression (CTR) is an appealing recent method taking this approach which tightly couples the two components that learn from two different sources of information. Nevertheless, the latent representation learned by CTR may not be very effective when the auxiliary information is very sparse. To address this problem, we generalize recent advances in deep learning from i.i.d. input to non-i.i.d. (CF-based) input and propose in this paper a hierarchical Bayesian model called collaborative deep learning (CDL), which jointly performs deep representation learning for the content information and collaborative filtering for the ratings (feedback) matrix. Extensive experiments on three real-world datasets from different domains show that CDL can significantly advance the state of the art