Globalization and Legal Information Management

Draft of Chapter 2 of the IALL International Handbook of Legal Information Management by Jules Winterton, Associate Director and Librarian, Institute of Advanced Legal Studies, University of London. This chapter is a relatively brief survey of what globalization means in the field of legal information management and what effect it has had and will have on a range of activities and policy areas relevant to the practice of legal information management. There are firstly some comments towards a definition of globalization for the purposes of this chapter and then a survey of the following in the light of that definition: legal systems, information consumers, legal information needs, information and management, legal publishing, digitization, intellectual property rights, lobbying and advocacy on policy issues (the politics of law librarianship), international networking, and legal information managers and law librarians of the future

Coincidental Generation

Generative AI models are emerging as a versatile tool across diverse industries with applications in synthetic data generation computational art personalization of products and services and immersive entertainment Here we introduce a new privacy concern in the adoption and use of generative AI models that of coincidental generation Coincidental generation occurs when a models output inadvertently bears a likeness to a realworld entity Consider for example synthetic portrait generators which are today deployed in commercial applications such as virtual modeling agencies and synthetic stock photography We argue that the low intrinsic dimensionality of human face perception implies that every synthetically generated face will coincidentally resemble an actual person all but guaranteeing a privacy violation in the form of a misappropriation of likeness

A study of two problems in data mining: anomaly monitoring and privacy preservation.

Bu, Yingyi.Thesis (M.Phil.)--Chinese University of Hong Kong, 2008.Includes bibliographical references (leaves 89-94).Abstracts in English and Chinese.Abstract --- p.iAcknowledgement --- p.vChapter 1 --- Introduction --- p.1Chapter 1.1 --- Anomaly Monitoring --- p.1Chapter 1.2 --- Privacy Preservation --- p.5Chapter 1.2.1 --- Motivation --- p.7Chapter 1.2.2 --- Contribution --- p.12Chapter 2 --- Anomaly Monitoring --- p.16Chapter 2.1 --- Problem Statement --- p.16Chapter 2.2 --- A Preliminary Solution: Simple Pruning --- p.19Chapter 2.3 --- Efficient Monitoring by Local Clusters --- p.21Chapter 2.3.1 --- Incremental Local Clustering --- p.22Chapter 2.3.2 --- Batch Monitoring by Cluster Join --- p.24Chapter 2.3.3 --- Cost Analysis and Optimization --- p.28Chapter 2.4 --- Piecewise Index and Query Reschedule --- p.31Chapter 2.4.1 --- Piecewise VP-trees --- p.32Chapter 2.4.2 --- Candidate Rescheduling --- p.35Chapter 2.4.3 --- Cost Analysis --- p.36Chapter 2.5 --- Upper Bound Lemma: For Dynamic Time Warping Distance --- p.37Chapter 2.6 --- Experimental Evaluations --- p.39Chapter 2.6.1 --- Effectiveness --- p.40Chapter 2.6.2 --- Efficiency --- p.46Chapter 2.7 --- Related Work --- p.49Chapter 3 --- Privacy Preservation --- p.52Chapter 3.1 --- Problem Definition --- p.52Chapter 3.2 --- HD-Composition --- p.58Chapter 3.2.1 --- Role-based Partition --- p.59Chapter 3.2.2 --- Cohort-based Partition --- p.61Chapter 3.2.3 --- Privacy Guarantee --- p.70Chapter 3.2.4 --- Refinement of HD-composition --- p.75Chapter 3.2.5 --- Anonymization Algorithm --- p.76Chapter 3.3 --- Experiments --- p.77Chapter 3.3.1 --- Failures of Conventional Generalizations --- p.78Chapter 3.3.2 --- Evaluations of HD-Composition --- p.79Chapter 3.4 --- Related Work --- p.85Chapter 4 --- Conclusions --- p.87Bibliography --- p.8

Privacy-by-design in big data analytics and social mining

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PRIVACY PRESERVING DATA MINING FOR NUMERICAL MATRICES, SOCIAL NETWORKS, AND BIG DATA

Motivated by increasing public awareness of possible abuse of confidential information, which is considered as a significant hindrance to the development of e-society, medical and financial markets, a privacy preserving data mining framework is presented so that data owners can carefully process data in order to preserve confidential information and guarantee information functionality within an acceptable boundary. First, among many privacy-preserving methodologies, as a group of popular techniques for achieving a balance between data utility and information privacy, a class of data perturbation methods add a noise signal, following a statistical distribution, to an original numerical matrix. With the help of analysis in eigenspace of perturbed data, the potential privacy vulnerability of a popular data perturbation is analyzed in the presence of very little information leakage in privacy-preserving databases. The vulnerability to very little data leakage is theoretically proved and experimentally illustrated. Second, in addition to numerical matrices, social networks have played a critical role in modern e-society. Security and privacy in social networks receive a lot of attention because of recent security scandals among some popular social network service providers. So, the need to protect confidential information from being disclosed motivates us to develop multiple privacy-preserving techniques for social networks. Affinities (or weights) attached to edges are private and can lead to personal security leakage. To protect privacy of social networks, several algorithms are proposed, including Gaussian perturbation, greedy algorithm, and probability random walking algorithm. They can quickly modify original data in a large-scale situation, to satisfy different privacy requirements. Third, the era of big data is approaching on the horizon in the industrial arena and academia, as the quantity of collected data is increasing in an exponential fashion. Three issues are studied in the age of big data with privacy preservation, obtaining a high confidence about accuracy of any specific differentially private queries, speedily and accurately updating a private summary of a binary stream with I/O-awareness, and launching a mutual private information retrieval for big data. All three issues are handled by two core backbones, differential privacy and the Chernoff Bound