IEEE-CSE Projects 2018

Aim: to identify on-site users of an event, from whom we could gather valuable information regarding the process of events and investigate suspects when an event is associated with crime or terrorist. Existing System: To identify on-site users, the traditional and straight forward way is to search users whose tweets are geo-tagged and located within a certain range (e.g., 200 meters) of the center location of a target social event. Drawback: ·          only 34% of Twitter users have meaningful location information in their profiles for on-site user identification and less than 1% of Twitter users tag their tweets with GPS location [2, 3]. ·          The sheer volume of social media users and their posts and the lack of user location information make it particularly challenging to identify on-site users. Proposed System: Fused Feature Gaussian Process Regression (FEGOR) model, which exploits three influential factors in social networks for on-site user identification

Aim:   propose a novel multi-variable privacy characterization and quantification model.  Existing system: Privacy-Preserving Data Publishing (PPDP) techniques (k-anonymity, l-diversity and t-closeness.) have been proposed in literature. However, they lack a proper privacy characterization and measurement. all the existing PPDP schemes have limitations in privacy characterization  Proposed System: PUBLISHING MODEL goals is to quantify privacy loss of individuals having other attribute values (other diseases) within the same class. PRIVACY CHARACTERIZATION proposes two privacy metrics that can measure privacy leakage from two different perspectives.  Methodology: The proposed framework consists of two steps . First, model attributes in a dataset as a multi-variable model. Based on first model, are able to re-define the prior and posterior adversarial belief about attribute values of individuals.  Then characterize privacy of these individuals based on the privacy risks attac

Aim: to systematically construct a secure and privacy preserving e-health cloud system, so that it is immune to the inference attack and runs efficiently. to protect the E-Healthcare data with fine-grained access control, the data can be either numerical or string value. Proposed system: proposed two-layer encryption scheme In the first-layer encryption, paper propose to define a specialized access policy for each data attribute in the EHR, generate a secret share or every distinct role attribute, and reconstruct the secret to encrypt each data attribute, which ensures a fine-grained access control, saves much encryption time, and conceals the frequency of role attributes occurring in the EHR. In the second-layer encryption, paper propose to preserve the privacy of role attributes and access policies used in the first-layer encryption. Specifically, merge the first-layer access policies, add noise to the merged access policy, and encrypt the first-layer access policies un