Privacy protected data forwarding in human associated delay tolerant networks

Human associated delay-tolerant networks (HDTNs) are new networks for DTNs, where mobile devices are associated with humans and demonstrate social related communication characteristics. As most of recent works use real social trace files to study the date forwarding in HDTNs, the privacy protection becomes a serious issue. Traditional privacy protections need to keep the attributes semantics, such as data mining and information retrieval. However, in HDTNs, it is not necessary to keep these meaningful semantics. In this paper, instead, we propose to anonymize the original data by coding to preserve individual's privacy and apply Privacy Protected Data Forwarding (PPDF) model to select the top N nodes to perform the multicast. We use both MIT Reality and Infocom 06 datasets, which are human associated mobile network trace file, to simulate our model. The results of our simulations show that this method can achieve a high data forwarding performance while protect the nodes' privacy as well.

Australia's digital policy : adopting a children's rights approach

  Children’s engagement with online technologies may seem second nature, yet the impact that the internet has on their lives is shaped by a powerful public policy agenda that largely overlooks children’s interests. Australia’s digital policy framework is dominated by discourses of safety and risk on the one hand and, on the other, neoliberal arguments about the possibilities for economic growth offered by e-commerce. In the midst of such powerful discourses it is difficult for children’s voices to be heard. This paper offers a close textual analysis of the Australian public policy context for regulating cyberspace. Finding a discursive duopoly that overlooks children’s interests, the author identifies two key features of a rights-based approach to challenge the dominant narratives currently serving the interests of the private sector and the State. 

Sitting behaviour-based pattern recognition for predicting driver fatigue

The proposed approach based on physiological characteristics of sitting behaviours and sophisticated machine learning techniques would enable an effective and practical solution to driver fatigue prognosis since it is insensitive to the illumination of driving environment, non-obtrusive to driver, without violating driver’s privacy, more acceptable by drivers.

Privacy preserving for tagging recommender systems

Tagging recommender systems allow Internet users to annotate resources with personalized tags. The connection among users, resources and these annotations, often called afolksonomy, permits users the freedom to explore tags, and to obtain recommendations. Releasing these tagging datasets accelerates both commercial and research work on recommender systems. However, adversaries may re-identify a user and her/his sensitivity information from the tagging dataset using a little background information. Recently, several private techniques have been proposed to address the problem, but most of them lack a strict privacy notion, and can hardly resist the number of possible attacks. This paper proposes an private releasing algorithm to perturb users' profile in a strict privacy notion, differential privacy, with the goal of preserving a user's identity in a tagging dataset. The algorithm includes three privacy preserving operations: Private Tag Clustering is used to shrink the randomized domain and Private Tag Selection is then applied to find the most suitable replacement tags for the original tags. To hide the numbers of tags, the third operation, Weight Perturbation, finally adds Lap lace noise to the weight of tags We present extensive experimental results on two real world datasets, Delicious and Bibsonomy. While the personalization algorithmis successful in both cases.

Guest editors' introduction : Special issue on trust, security, and privacy in parallel and distributed systems

In modern computing paradigms, most computing systems, e.g., cluster computing, grid computing, cloud computing, the Internet, telecommunication networks, Cyber- Physical Systems (CPS), and Machine-to-Machine communication networks (M2M), are parallel and distributed systems. While providing improved expandability, manageability, efficiency, and reliability, parallel and distributed systems increase their security weaknesses to an unprecedented scale. As the system devices are widely connected, their vulnerabilities are shared by the entire system. Because tasks are allocated to, and information is exchanged among the system devices that may belong to different users, trust, security, and privacy issues have yet to be resolved. This special issue of the IEEE Transactions on Parallel and Distributed Systems (TPDS) highlights recent advances in trust, security, and privacy for emerging parallel and distributed systems. This special issue was initiated by Dr. Xu Li, Dr. Patrick McDaniel, Dr. Radha Poovendran, and Dr. Guojun Wang. Due to a large number of submissions, Dr. Zhenfu Cao, Dr. Keqiu Li, and Dr. Yang Xiang were later invited to the editorial team. Dr. Xu Li was responsible for coordinating the paper review process. In response to the call for papers, we received 150 effective submissions, out of which 24 are included in this special issue after rigorous review and careful revision, presenting an acceptance ratio of 16 percent. The accepted papers are divided into three groups, covering issues related to trust, security, and privacy, respectively.

Gaming privacy : a Canadian case study of a children’s co-created privacy literacy game

This paper reports the process and outcomes of the design of a game that educates children about management of privacy online. Using a participatory action research process, children worked with the researchers to develop and play a game which simulates certain aspects of online privacy management and allows for scaffolded experiential learning in a safe environment. The game allows children to develop autonomous skills and understandings, not only for more effective learning but also because it is only through autonomy that children can develop a sense of self which is necessary for understanding what it means to be private. The paper shows that children have quite sophisticated understandings of privacy, compared with some adult perceptions, and that these understandings include awareness of the risks posed by commercial organisations seeking to gather personal data from them. The paper shows how engaging children as research and design participants can lead to more successful approaches in the development of privacy literacy.

Generating repudiable, memorizable and privacy preserving security questions using the Propp Theory of Narrative

 Security questions are often based on personal information that is limited in variety, available in the public record and very difficult to change if compromised. A personalized folktale shared only by the communicating parties provides memorizable basis for individualized security questions that can be readily replaced in the event of a security breach. We utilize the Propp theory of narrative to provide a basis of abstraction for story generation systems. We develop a proof-of-concept system based on placeholder replacement to demonstrate the generation of repudiate and memorizable questions and answers suitable for online security questions. A 3-component protocol is presented that demonstrates the use of this process to derive a shared secret key through privacy amplification. This combination of story generation and communication security provides the basis for improvements in current security question practice.