Case study: disclosure of indirect device fingerprinting in privacy policies

Recent developments in online tracking make it harder for individuals to detect and block trackers. This is especially true for de- vice fingerprinting techniques that websites use to identify and track individual devices. Direct trackers { those that directly ask the device for identifying information { can often be blocked with browser configu- rations or other simple techniques. However, some sites have shifted to indirect tracking methods, which attempt to uniquely identify a device by asking the browser to perform a seemingly-unrelated task. One type of indirect tracking known as Canvas fingerprinting causes the browser to render a graphic recording rendering statistics as a unique identifier. Even experts find it challenging to discern some indirect fingerprinting methods. In this work, we aim to observe how indirect device fingerprint- ing methods are disclosed in privacy policies, and consider whether the disclosures are sufficient to enable website visitors to block the track- ing methods. We compare these disclosures to the disclosure of direct fingerprinting methods on the same websites. Our case study analyzes one indirect ngerprinting technique, Canvas fingerprinting. We use an existing automated detector of this fingerprint- ing technique to conservatively detect its use on Alexa Top 500 websites that cater to United States consumers, and we examine the privacy poli- cies of the resulting 28 websites. Disclosures of indirect fingerprinting vary in specificity. None described the specific methods with enough granularity to know the website used Canvas fingerprinting. Conversely, many sites did provide enough detail about usage of direct fingerprint- ing methods to allow a website visitor to reliably detect and block those techniques. We conclude that indirect fingerprinting methods are often technically difficult to detect, and are not identified with specificity in legal privacy notices. This makes indirect fingerprinting more difficult to block, and therefore risks disturbing the tentative armistice between individuals and websites currently in place for direct fingerprinting. This paper illustrates differences in fingerprinting approaches, and explains why technologists, technology lawyers, and policymakers need to appreciate the challenges of indirect fingerprinting.Accepted manuscrip

Ethical Reflections of Human Brain Research and Smart Information Systems

open access journalThis case study explores ethical issues that relate to the use of Smart Infor-mation Systems (SIS) in human brain research. The case study is based on the Human Brain Project (HBP), which is a European Union funded project. The project uses SIS to build a research infrastructure aimed at the advancement of neuroscience, medicine and computing. The case study was conducted to assess how the HBP recognises and deal with ethical concerns relating to the use of SIS in human brain research. To under-stand some of the ethical implications of using SIS in human brain research, data was collected through a document review and three semi-structured interviews with partic-ipants from the HBP. Results from the case study indicate that the main ethical concerns with the use of SIS in human brain research include privacy and confidentiality, the security of personal data, discrimination that arises from bias and access to the SIS and their outcomes. Furthermore, there is an issue with the transparency of the processes that are involved in human brain research. In response to these issues, the HBP has put in place different mechanisms to ensure responsible research and innovation through a dedicated pro-gram. The paper provides lessons for the responsible implementation of SIS in research, including human brain research and extends some of the mechanisms that could be employed by researchers and developers of SIS for research in addressing such issues

A Risk Management Process for Consumers

Simply by using information technology, consumers expose themselves to considerable security risks. Because no technical or legal solutions are readily available, the only remedy is to develop a risk management process for consumers, similar to the process executed by enterprises. Consumers need to consider the risks in a structured way, and take action, not once, but iteratively. Such a process is feasible: enterprises already execute such processes, and time-saving tools can support the consumer in her own process. In fact, given our society's emphasis on individual responsibilities, skills and devices, a risk management process for consumers is the logical next step in improving information security

Security and Online learning: to protect or prohibit

The rapid development of online learning is opening up many new learning opportunities. Yet, with this increased potential come a myriad of risks. Usable security systems are essential as poor usability in security can result in excluding intended users while allowing sensitive data to be released to unacceptable recipients. This chapter presents findings concerned with usability for two security issues: authentication mechanisms and privacy. Usability issues such as memorability, feedback, guidance, context of use and concepts of information ownership are reviewed within various environments. This chapter also reviews the roots of these usability difficulties in the culture clash between the non-user-oriented perspective of security and the information exchange culture of the education domain. Finally an account is provided of how future systems can be developed which maintain security and yet are still usable

Security and Privacy Issues of Big Data

This chapter revises the most important aspects in how computing infrastructures should be configured and intelligently managed to fulfill the most notably security aspects required by Big Data applications. One of them is privacy. It is a pertinent aspect to be addressed because users share more and more personal data and content through their devices and computers to social networks and public clouds. So, a secure framework to social networks is a very hot topic research. This last topic is addressed in one of the two sections of the current chapter with case studies. In addition, the traditional mechanisms to support security such as firewalls and demilitarized zones are not suitable to be applied in computing systems to support Big Data. SDN is an emergent management solution that could become a convenient mechanism to implement security in Big Data systems, as we show through a second case study at the end of the chapter. This also discusses current relevant work and identifies open issues.Comment: In book Handbook of Research on Trends and Future Directions in Big Data and Web Intelligence, IGI Global, 201

Privacy-Preserving Trust Management Mechanisms from Private Matching Schemes

Cryptographic primitives are essential for constructing privacy-preserving communication mechanisms. There are situations in which two parties that do not know each other need to exchange sensitive information on the Internet. Trust management mechanisms make use of digital credentials and certificates in order to establish trust among these strangers. We address the problem of choosing which credentials are exchanged. During this process, each party should learn no information about the preferences of the other party other than strictly required for trust establishment. We present a method to reach an agreement on the credentials to be exchanged that preserves the privacy of the parties. Our method is based on secure two-party computation protocols for set intersection. Namely, it is constructed from private matching schemes.Comment: The material in this paper will be presented in part at the 8th DPM International Workshop on Data Privacy Management (DPM 2013