Privacy-Preserving Enhanced Collaborative Tagging

Privacy-Preserving Enhanced Collaborative Tagging

Abstract:

                Collaborative tagging is one of the most popular services available online, and it allows end user to loosely classify either online or offline resources based on their feedback, expressed in the form of free-text labels (i.e., tags). Although tags may not be per se sensitive information, the wide use of collaborative tagging services increases the risk of cross referencing, thereby seriously compromising user privacy. In this paper, we make a first contribution toward the development of a privacy-preserving collaborative tagging service, by showing how a specific privacy-enhancing technology, namely tag suppression, can be used to protect end-user privacy. Moreover, we analyze how our approach can affect the effectiveness of a policy-based collaborative tagging system that supports enhanced web access functionalities, like content filtering and discovery, based on preferences specified by end users.

Existing System:

COLLABORATIVE tagging is one of the most diffused and popular services available online. First provided by social bookmarking sites only—for example, Delicious (http://delicious.com), Digg (http://digg.com), Stumble- Upon (http://stumbleupon.com)—it is currently supported by nearly any type of social web application, and it is used to annotate any kind of online and offline resources (e.g., webpages, images, videos, movies, music, and even blog posts).

Disadvantage:

                                            Previous System does not provide proper privacy.

Proposed System:

                                 The main purpose of collaborative tagging is to loosely classify resources based on end-user’s feedback, expressed in the form of free-text labels (i.e., tags). The novelty of such an approach to content/resource categorization has been seen, in recent years, as a challenging research topic. In fact, collaborative tagging may be the basis for a semantic network connecting online resources based on their characteristics, and not only their URIs. At the same time, the undefined semantics of tags, which are per same ambiguous and expressed in multiple languages, makes it difficult to enforce semantic interoperability and to grant a reasonable level of accuracy when determining the “meaning” of a tag.

Advantage:

                                 Here we proposed the secured system, no one can hack the system .