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Full-Text Articles in Databases and Information Systems
A Multi-User Steganographic File System On Untrusted Shared Storage, Jin Han, Meng Pan, Debin Gao, Hwee Hwa Pang
A Multi-User Steganographic File System On Untrusted Shared Storage, Jin Han, Meng Pan, Debin Gao, Hwee Hwa Pang
Research Collection School Of Computing and Information Systems
Existing steganographic file systems enable a user to hide the existence of his secret data by claiming that they are (static) dummy data created during disk initialization. Such a claim is plausible if the adversary only sees the disk content at the point of attack. In a multi-user computing environment that employs untrusted shared storage, however, the adversary could have taken multiple snapshots of the disk content over time. Since the dummy data are static, the differences across snapshots thus disclose the locations of user data, and could even reveal the user passwords. In this paper, we introduce a Dummy-Relocatable …
Embellishing Text Search Queries To Protect User Privacy, Hwee Hwa Pang, Xuhua Ding, Xiaokui Xiao
Embellishing Text Search Queries To Protect User Privacy, Hwee Hwa Pang, Xuhua Ding, Xiaokui Xiao
Research Collection School Of Computing and Information Systems
Users of text search engines are increasingly wary that their activities may disclose confidential information about their business or personal profiles. It would be desirable for a search engine to perform document retrieval for users while protecting their intent. In this paper, we identify the privacy risks arising from semantically related search terms within a query, and from recurring highspecificity query terms in a search session. To counter the risks, we propose a solution for a similarity text retrieval system to offer anonymity and plausible deniability for the query terms, and hence the user intent, without degrading the system’s precision-recall …
Learning User Profiles For Personalized Information Dissemination, Ah-Hwee Tan, Christine Teo
Learning User Profiles For Personalized Information Dissemination, Ah-Hwee Tan, Christine Teo
Research Collection School Of Computing and Information Systems
Personalized information systems represent the recent effort of delivering information to users more effectively in the modern electronic age. This paper illustrates how a supervised Adaptive Resonance Theory (ART) system, known as fuzzy ARAM, can be used to learn user profiles for personalized information dissemination. ARAM learning is on-line, fast, and incremental. Acquisition of new knowledge does not require re-training on previously learned cases. ARAM integrates both user-defined and system-learned knowledge in a single framework. Therefore inconsistency between the two knowledge sources will not arise. ARAM has been used to develop a personalized news system known as PIN. Preliminary experiments …