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For those with legitimate reason to use the Internet anonymously--diplomats, military and other government agencies, journalists, political activists, IT professionals, law enforcement personnel, political refugees and others--anonymous networking provides an invaluable tool, and many good reasons that anonymity can serve a very important purpose. Anonymous use of the Internet is made difficult by the many websites that know everything about us, by the cookies and ad networks, IP-logging ISPs, even nosy officials may get involved. It is no longer possible to turn off browser cookies to be left alone in your online life. Practical Anonymity: Hiding in Plain Sight Online shows you how to use the most effective and widely-used anonymity tools--the ones that protect diplomats, military and other government agencies to become invisible online. This practical guide skips the theoretical and technical details and focuses on getting from zero to anonymous as fast as possible. For many, using any of the open-source, peer-reviewed tools for connecting to the Internet via an anonymous network may be (or seem to be) too difficult because most of the information about these tools is burdened with discussions of how they work and how to maximize security. Even tech-savvy users may find the burden too great--but actually using the tools can be pretty simple. The primary market for this book consists of IT professionals who need/want tools for anonymity to test/work around corporate firewalls and router filtering as well as provide anonymity tools to their customers. Simple, step-by-step instructions for configuring and using anonymous networking software - Simple, step-by-step instructions for configuring and using anonymous networking software - Use of open source, time-proven and peer-reviewed tools for anonymity - Plain-language discussion of actual threats and concrete suggestions for appropriate responses - Easy-to-follow tips for safer computing - Simple, step-by-step instructions for configuring and using anonymous networking software - Use of open source, time-proven and peer-reviewed tools for anonymity - Plain-language discussion of actual threats, and concrete suggestions for appropriate responses - Easy to follow tips for safer computing
The effect of a criminal record or arrest can be long-lasting and damaging. Setting out the steps that can help clients to navigate the effect of their criminal record, improve their job prospects, and protect against harmful disclosure of their private life. Criminal Records, Privacy and the Criminal Justice System: A Handbook is a primer on the law and available applications to be taken for clients relating to privacy, criminal records, historic convictions, and reputation management in the criminal justice sector. The authors guide you through the steps that can be taken to delete police records, challenge the content of criminal record certificates, expunge criminal cautions, and bring claims protecting the privacy and data protection rights of clients. As the only handbook of its kind, addressing public and private law claims under one title, this brand new book gives an holistic overview of the ways in which lawyers can help clients cope with the impact of the criminal justice system on their lives and reputations. As such, it is an essential guide for criminal and public law solicitors and barristers, law centres, CABs and PR firms.
This e-book discusses the issues surrounding informational privacy - assuming that privacy is the indefeasible right of an individual to control the ways in which personal information is obtained, processed, distributed, shared and used by any other entity. The review of current research work in the area of user privacy has indicated that the path for user privacy protection is through the four basic privacy requirements namely anonymity, pseudonymity, unlinkability and unobservability. By addressing these four basic requirements one aims to minimize the collection of user identifiable data.
There are several anonymity architectures for Internet communication in use today. They are either unsafe or very complex. In this work the design, implementation and evaluation of an anonymity architecture that provides a high level of protection and is still simple enough to enable high-bandwidth, low-latency Internet communications is presented. The architecture uses a single-node anonymity service provider in combination with anonymity groups. The software components of the architecture consist of a client program for end-users, a server program for the anonymity service provider and a remote management component for the server program. To enable a high-bandwidth and low-latency communication between the client program and the server program a new high-performance IO-framework was designed and implemented.
There are few more important areas of current research than this, and here, Springer has published a double helping of the latest work in the field. That’s because the book contains the thoroughly refereed proceedings of the 11th International Conference on Financial Cryptography and Data Security, and the co-located 1st International Workshop on Usable Security, both held in Trinidad/Tobago in February 2007. Topics covered include payment systems and authentication.
This two-volume set LNICST 398 and 399 constitutes the post-conference proceedings of the 17th International Conference on Security and Privacy in Communication Networks, SecureComm 2021, held in September 2021. Due to COVID-19 pandemic the conference was held virtually. The 56 full papers were carefully reviewed and selected from 143 submissions. The papers focus on the latest scientific research results in security and privacy in wired, mobile, hybrid and ad hoc networks, in IoT technologies, in cyber-physical systems, in next-generation communication systems in web and systems security and in pervasive and ubiquitous computing.
This book provides an overview of the research work on data privacy and privacy enhancing technologies carried by the participants of the ARES project. ARES (Advanced Research in Privacy an Security, CSD2007-00004) has been one of the most important research projects funded by the Spanish Government in the fields of computer security and privacy. It is part of the now extinct CONSOLIDER INGENIO 2010 program, a highly competitive program which aimed to advance knowledge and open new research lines among top Spanish research groups. The project started in 2007 and will finish this 2014. Composed by 6 research groups from 6 different institutions, it has gathered an important number of researchers during its lifetime. Among the work produced by the ARES project, one specific work package has been related to privacy. This books gathers works produced by members of the project related to data privacy and privacy enhancing technologies. The presented works not only summarize important research carried in the project but also serve as an overview of the state of the art in current research on data privacy and privacy enhancing technologies.
Keep sensitive user data safe and secure without sacrificing the performance and accuracy of your machine learning models. In Privacy Preserving Machine Learning, you will learn: Privacy considerations in machine learning Differential privacy techniques for machine learning Privacy-preserving synthetic data generation Privacy-enhancing technologies for data mining and database applications Compressive privacy for machine learning Privacy-Preserving Machine Learning is a comprehensive guide to avoiding data breaches in your machine learning projects. You’ll get to grips with modern privacy-enhancing techniques such as differential privacy, compressive privacy, and synthetic data generation. Based on years of DARPA-funded cybersecurity research, ML engineers of all skill levels will benefit from incorporating these privacy-preserving practices into their model development. By the time you’re done reading, you’ll be able to create machine learning systems that preserve user privacy without sacrificing data quality and model performance. About the Technology Machine learning applications need massive amounts of data. It’s up to you to keep the sensitive information in those data sets private and secure. Privacy preservation happens at every point in the ML process, from data collection and ingestion to model development and deployment. This practical book teaches you the skills you’ll need to secure your data pipelines end to end. About the Book Privacy-Preserving Machine Learning explores privacy preservation techniques through real-world use cases in facial recognition, cloud data storage, and more. You’ll learn about practical implementations you can deploy now, future privacy challenges, and how to adapt existing technologies to your needs. Your new skills build towards a complete security data platform project you’ll develop in the final chapter. What’s Inside Differential and compressive privacy techniques Privacy for frequency or mean estimation, naive Bayes classifier, and deep learning Privacy-preserving synthetic data generation Enhanced privacy for data mining and database applications About the Reader For machine learning engineers and developers. Examples in Python and Java. About the Author J. Morris Chang is a professor at the University of South Florida. His research projects have been funded by DARPA and the DoD. Di Zhuang is a security engineer at Snap Inc. Dumindu Samaraweera is an assistant research professor at the University of South Florida. The technical editor for this book, Wilko Henecka, is a senior software engineer at Ambiata where he builds privacy-preserving software. Table of Contents PART 1 - BASICS OF PRIVACY-PRESERVING MACHINE LEARNING WITH DIFFERENTIAL PRIVACY 1 Privacy considerations in machine learning 2 Differential privacy for machine learning 3 Advanced concepts of differential privacy for machine learning PART 2 - LOCAL DIFFERENTIAL PRIVACY AND SYNTHETIC DATA GENERATION 4 Local differential privacy for machine learning 5 Advanced LDP mechanisms for machine learning 6 Privacy-preserving synthetic data generation PART 3 - BUILDING PRIVACY-ASSURED MACHINE LEARNING APPLICATIONS 7 Privacy-preserving data mining techniques 8 Privacy-preserving data management and operations 9 Compressive privacy for machine learning 10 Putting it all together: Designing a privacy-enhanced platform (DataHub)
This book constitutes the thoroughly refereed postproceedings of the 5th International Workshop on Privacy Enhancing Technologies, PET 2006, held in Cavtat, Croatia, in May and June 2005. The 17 revised full papers presented were carefully selected from 74 submissions during two rounds of reviewing and improvement. The papers address most current privacy enhancing technologies in various application contexts.