Privacy-preserving targeted advertising scheme for IPTV using the cloud

In this paper, we present a privacy-preserving scheme for targeted advertising via the Internet Protocol TV (IPTV). The scheme uses a communication model involving a collection of viewers/subscribers, a content provider (IPTV), an advertiser, and a cloud server. To provide high quality directed advertising service, the advertiser can utilize not only demographic information of subscribers, but also their watching habits. The latter includes watching history, preferences for IPTV content and watching rate, which are published on the cloud server periodically (e.g. weekly) along with anonymized demographics. Since the published data may leak sensitive information about subscribers, it is safeguarded using cryptographic techniques in addition to the anonymization of demographics. The techniques used by the advertiser, which can be manifested in its queries to the cloud, are considered (trade) secrets and therefore are protected as well. The cloud is oblivious to the published data, the queries of the advertiser as well as its own responses to these queries. Only a legitimate advertiser, endorsed with a so-called {\em trapdoor} by the IPTV, can query the cloud and utilize the query results. The performance of the proposed scheme is evaluated with experiments, which show that the scheme is suitable for practical usage

DYNAMIC SEARCHABLE OVER ENCRYPTED CLOUD DATA FOR MULTI KEYWORD RANKED SEARCH SCHEME

As a result of rising status of cloud computing, increasingly more information proprietors tend to be provoked to subcontract their data to cloud machines for huge expediency and cost this is certainly abridged information company. However, responsive information must be encrypted before outsourcing for solitude needs, which obsoletes data operation akin to document retrieval that is keyword-based. In this article, we truth be told there a cramped multi-keyword ranked research method over encrypted cloud data, which simultaneously chains modernize this is certainly lively like removal and insertion of papers. Particularly, the vector space model and also the TF this is certainly widely-used IDF are mutual in the index building and query generation. We produce a certain directory site this is certainly tree-based and recommend a “Greedy Depth-first Search” algorithm to give efficient multi-keyword rated search. The kNN that is secure is useful to encrypt the index and query vectors, and meanwhile guarantee precise value score calculation between encrypted index and query vectors. To be able to withstand attacks which are numerical apparition terms are added to the index vector for blinding search results. As a result of utilize of your certain index this is certainly tree-based, the planned system can realize sub-linear search time and contract with the removal and introduction of documents athletically. Extensive experiments are carried out showing the competence associated with suggested plan

Ontology Based Semantic Web Information Retrieval Enhancing Search Significance

The web contain huge amount of structured as well as unstructured data/information. This varying nature of data may yield a retrieval response that is expected to contain relevant response that is expected to contain relevant as well as irrelevant data while directing search. In order to filter out irrelevance in the search result, numerous methodologies have been used to extract more and more relevant search responses in retrieval. This work has adopted semantic search dealing directly with the knowledge base. The approach incorporates Query pattern evolution and semantic keyword matching with final detail to enhance significance of relevant data retrieval. The proposed method is implemented in open source computing tool environment and the result obtained thereof are compared with that of earlier used methodologies

Multi OwnerSecret Key Generation for Ranked Multi-Keyword Search in Cloud

For privacy concerns, secure searches over encrypted cloud data has motivated several research works under the single owner model. However, most cloud servers in practice do not just serve one owner; instead, they support multiple owners to share the benefits brought by cloud computing. The issue of recovering the encrypted data over the cloud is mind boggling. Numerous search procedures are utilized for recovering the scrambled data from cloud. This paper axes around an arrangement of keyword Search instruments over encrypted data, which gives secured data recovery high proficiency. Search over encrypted data is a method of extraordinary enthusiasm for the cloud computing time, in light of the fact that numerous trust that delicate data must be scrambled before outsourcing to the cloud servers with a specific end goal to guarantee client data security. Concocting a productive and secure search scheme over scrambled data includes strategies from ple spaces. It presumes that, keyword search is intended to be best methodology for searching the encrypted data in the Cloud. It gives more productivity than single keyword search