Cloud based services provide scalable storage
capacities and enormous computing capability to enterprises and
individuals to support big data operations in different sectors like
banking, scientific research and health care. Therefore many data
owners are interested to outsource their data to cloud storage
servers due to their huge advantage in data processing. However,
as the banking and health records usually contain sensitive data,
there are privacy concerns if the data gets leaked to un-trusted
third parties in cloud storage. To protect data from leakage, the
widely used technique is to encrypt the data before uploading into
cloud storage servers. The traditional methods implemented by
many authors consumes more time to outsource the data and
searching for a document is also time consuming. Sometimes
there may be chances of data leakage due to insufficient security.
To resolve these issues, in the current VPSearch(VPS) scheme is
implemented, which provides features like verifiability of search
results and privacy preservation. With its features the current
system consumes more time for file uploading and index
generation, which slows down the searching process. In the
existing VPS scheme time minimization to efficiently search for a
particular document is a challenging task on the cloud. To resolve
all the above drawbacks, we have designed an index generation
scheme using a tree structure along with a search algorithm using
Greedy Depth-first technique, that reduces the time for uploading
files and file searching time. The newly implemented scheme
minimizes the time required to form the index tree file for set of
files in the document which are to be uploaded and helps in
storing the files in a index tree format. These techniques result in
reducing the document upload time and speeding up the process
of accessing data efficiently using multi-keyword search with
top-'K' value.