Implementation on Health Care Database Mining in Outsourced Database

Due to the EMR (Electronic Medical Record) system there will be a rapid growth in health data collection. As we have already discuss in previous review paper the different work of the health care data record for maintaining the privacy and security of health care most private data. Now in this paper we are going to implement sheltered and secretive data management structure that addresses both the sheltered and secretive issues in the managementor organization of medical datainoutsourceddatabases. Theproposed framework will assure the security of data by using semantically secure encryption schemes to keep data encrypted in outsourced databases. The framework also provides a differentially-private query or uncertainty interface that can support a number of SQL queries and complicated data mining responsibilities. We are using a multiparty algorithm for this purpose. So that all the purpose is to make a secure and private management system for medical data or record storage and accesses

Authenticating Aggregate Range Queries over Dynamic Multidimensional Dataset

We are interested in the integrity of the query results from an outsourced database service provider. Alice passes a set $\set{D}$ of $d$-dimensional points, together with some authentication tag $\set{T}$, to an untrusted service provider Bob. Later, Alice issues some query over $\set{D}$ to Bob, and Bob should produce a query result and a proof based on $\set{D}$ and $\set{T}$. Alice wants to verify the integrity of the query result with the help of the proof, using only the private key. Xu J.~\emph{et al.}~\cite{maia-full} proposed an authentication scheme to solve this problem for multidimensional aggregate range query, including {\SUM, \COUNT, \MIN, \MAX} and {\MEDIAN}, and multidimensional range selection query, with $O(d^2)$ communication overhead. However, their scheme only applys to static database. This paper extends their method to support dynamic operations on the dataset, including inserting or deleting a point from the dataset. The communication overhead of our scheme is $O(d^2 \log N)$, where $N$ is the number of data points in the dataset

Health Participatory Sensing Networks for Mobile Device Public Health Data Collection and Intervention

The pervasive availability and increasingly sophisticated functionalities of smartphones and their connected external sensors or wearable devices can provide new data collection capabilities relevant to public health. Current research and commercial efforts have concentrated on sensor-based collection of health data for personal fitness and personal healthcare feedback purposes. However, to date there has not been a detailed investigation of how such smartphones and sensors can be utilized for public health data collection. Unlike most sensing applications, in the case of public health, capturing comprehensive and detailed data is not a necessity, as aggregate data alone is in many cases sufficient for public health purposes. As such, public health data has the characteristic of being capturable whilst still not infringing privacy, as the detailed data of individuals that may allow re-identification is not needed, but rather only aggregate, de-identified and non-unique data for an individual. These types of public health data collection provide the challenge of the need to be flexible enough to answer a range of public health queries, while ensuring the level of detail returned preserves privacy. Additionally, the distribution of public health data collection request and other information to the participants without identifying the individual is a core requirement. An additional requirement for health participatory sensing networks is the ability to perform public health interventions. As with data collection, this needs to be completed in a non-identifying and privacy preserving manner. This thesis proposes a solution to these challenges, whereby a form of query assurance provides private and secure distribution of data collection requests and public health interventions to participants. While an additional, privacy preserving threshold approach to local processing of data prior to submission is used to provide re-identification protection for the participant. The evaluation finds that with manageable overheads, minimal reduction in the detail of collected data and strict communication privacy; privacy and anonymity can be preserved. This is significant for the field of participatory health sensing as a major concern of participants is most often real or perceived privacy risks of contribution