2,171 research outputs found

    User's Privacy in Recommendation Systems Applying Online Social Network Data, A Survey and Taxonomy

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    Recommender systems have become an integral part of many social networks and extract knowledge from a user's personal and sensitive data both explicitly, with the user's knowledge, and implicitly. This trend has created major privacy concerns as users are mostly unaware of what data and how much data is being used and how securely it is used. In this context, several works have been done to address privacy concerns for usage in online social network data and by recommender systems. This paper surveys the main privacy concerns, measurements and privacy-preserving techniques used in large-scale online social networks and recommender systems. It is based on historical works on security, privacy-preserving, statistical modeling, and datasets to provide an overview of the technical difficulties and problems associated with privacy preserving in online social networks.Comment: 26 pages, IET book chapter on big data recommender system

    Fool\u27s Gold: An Illustrated Critique of Differential Privacy

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    Differential privacy has taken the privacy community by storm. Computer scientists developed this technique to allow researchers to submit queries to databases without being able to glean sensitive information about the individuals described in the data. Legal scholars champion differential privacy as a practical solution to the competing interests in research and confidentiality, and policymakers are poised to adopt it as the gold standard for data privacy. It would be a disastrous mistake. This Article provides an illustrated guide to the virtues and pitfalls of differential privacy. While the technique is suitable for a narrow set of research uses, the great majority of analyses would produce results that are beyond absurd--average income in the negative millions or correlations well above 1.0, for example. The legal community mistakenly believes that differential privacy can offer the benefits of data research without sacrificing privacy. In fact, differential privacy will usually produce either very wrong research results or very useless privacy protections. Policymakers and data stewards will have to rely on a mix of approaches--perhaps differential privacy where it is well suited to the task and other disclosure prevention techniques in the great majority of situations where it isn\u27t

    In the Spirit of Salvation: William of St. Thierry’s Theological Treatment of Salvation in light of his Pneumatology

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    While desire for salvation forms the foundation of all Christian investigation, the modes through which salvation is explored vary between different theologians. William of St. Thierry, while leaving behind a wealth of extant sources, is frequently overlooked in the academic and theological investigation of the subject. This study undertakes an in-depth investigation of William’s writings, focused on pnuematological soteriology and an explanation of the characteristic elements which made up his thinking on this core. William investigates the Holy Spirit through three major identities: Will, Love and Unity. As a result of the fact that these characteristics also exist within humanity, and of the intimacy of the subject matter, this study is informative both to those studying historical theology, and to those seeking the spiritual origins of western anthropology and identity. In order to reveal the particular contours of William’s theology, it is important to compare him to the theologians on which he drew, and to those in whose company he was writing. This study compares William with the two patristic thinkers who exerted the greatest influence on his work: Origen of Antioch and St. Augustine of Hippo. It also draws comparison with four of William’s contemporaries, each representing different intellectual communities of the time: St. Anselm of Canterbury, St. Bernard of Clairvaux, Hugh of St. Victor, and Peter Abelard. This comparison is important in order to appreciate William’s theology in light of its own principles

    The misty crystal ball: Efficient concealment of privacy-sensitive attributes in predictive analytics

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    Individuals are becoming increasingly concerned with privacy. This curtails their willingness to share sensitive attributes like age, gender or personal preferences; yet firms largely rely upon customer data in any type of predictive analytics. Hence, organizations are confronted with a dilemma in which they need to make a tradeoff between a sparse use of data and the utility from better predictive analytics. This paper proposes a masking mechanism that obscures sensitive attributes while maintaining a large degree of predictive power. More precisely, we efficiently identify data partitions that are best suited for (i) shuffling, (ii) swapping and, as a form of randomization, (iii) perturbing attributes by conditional replacement. By operating on data partitions that are derived from a predictive algorithm, we achieve the objective of masking privacy-sensitive attributes with marginal downsides for predictive modeling. The resulting trade-off between masking and predictive utility is empirically evaluated in the context of customer churn where, for instance, a stratified shuffling of attribute values impedes predictive accuracy rarely by more than a percentage point. Our proposed framework entails direct managerial implications as a growing share of firms adopts predictive analytics and thus requires mechanisms that better adhere to user demands for information privacy
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