In what ways does policy on academic integrity, copyright and privacy need to respond in order to accommodate assessment with Web 2.0 tools?

Increasingly social web technologies, such as blogging and micro-blogging, audio and video podcasting, photo/video, social bookmarking, social networking, wiki writing or virtual worlds are being used as forms of authoring or content creation to support students’ learning in higher education. As Web 2.0 teaching practice is characterised by open access to information and collaborative networks there are both familiar and novel challenges for policy-makers in higher education institutions. The Government 2.0 Taskforce heralded legislative and practice changes necessary because of Web 2.0. We reflect on the qualitative feedback received from innovative higher education practitioners using Web 2.0 to assess student work. This indicates a need for information policy review to accommodate the cultural shift towards information exchange and communication across traditional institutional boundaries. Issues involved when implementing Web 2.0 assessments are identified to highlight requisite areas for policy improvement in higher education, in particular for academic integrity, copyright and privacy policies

An efficient solution for privacy-preserving, secure remote access to sensitive data

Sharing data that contains personally identifiable or sensitive information, such as medical records, always has privacy and security implications. The issues can become rather complex when the methods of access can vary, and accurate individual data needs to be provided whilst mass data release for specific purposes (for example for medical research) also has to be catered for. Although various solutions have been proposed to address the different aspects individually, a comprehensive approach is highly desirable. This paper presents a solution for maintaining the privacy of data released en masse in a controlled manner, and for providing secure access to the original data for authorized users. The results show that the solution is provably secure and maintains privacy in a more efficient manner than previous solutions.

Differential privacy for neighborhood-based collaborative filtering

As a popular technique in recommender systems, Collaborative Filtering (CF) has received extensive attention in recent years. However, its privacy-related issues, especially for neighborhood-based CF methods, can not be overlooked. The aim of this study is to address the privacy issues in the context of neighborhood-based CF methods by proposing a Private Neighbor Collaborative Filtering (PNCF) algorithm. The algorithm includes two privacy-preserving operations: Private Neighbor Selection and Recommendation-Aware Sensitivity. Private Neighbor Selection is constructed on the basis of the notion of differential privacy to privately choose neighbors. Recommendation-Aware Sensitivity is introduced to enhance the performance of recommendations. Theoretical and experimental analysis are provided to show the proposed algorithm can preserve differential privacy while retaining the accuracy of recommendations.

ID scanners and uberveillance in the night-time economy: crime prevention or invasion of privacy?

ID scanners are promoted as an effective solution to the problems of anti-social behavior and violence in many urban nighttime economies. However, the acceptance of this and other forms of computerized surveillance to prevent crime and anti-social behavior is based on several unproven assumptions. After outlining what ID scanners are and how they are becoming a normalized precondition of entry into one Australian nighttime economy, this chapter demonstrates how technology is commonly viewed as the key to preventing crime despite recognition of various problems associated with its adoption. The implications of technological determinism amongst policy makers, police, and crime prevention theories are then critically assessed in light of several issues that key informants talking about the value of ID scanners fail to mention when applauding their success. Notably, the broad, ill-defined, and confused notion of “privacy” is analyzed as a questionable legal remedy for the growing problems of überveillance.

Towards designing privacy-preserving signature-based IDS as a service : a study and practice

With the advent of Cloud Computing, IDS as a service (IDSaaS) has been proposed as an alternative to protect a network (e.g., financial organization) from a wide range of network attacks by offloading the expensive operations such as the process of signature matching to the cloud. The IDSaaS can be roughly classified into two types: signature-based detection and anomaly-based detection. During the packet inspection, no party wants to disclose their own data especially sensitive information to others, even to the cloud provider, for privacy concerns. However, current solutions of IDSaaS have not much discussed this issue. In this work, focus on the signature-based IDSaaS, we begin by designing a promising privacy-preserving intrusion detection mechanism, the main feature of which is that the process of signature matching does not reveal any specific content of network packets by means of a fingerprint-based comparison. We further conduct a study to evaluate this mechanism under a cloud scenario and identify several open problems and issues for designing such a privacy-preserving mechanism for IDSaaS in a practical environment.