combining top down and bottom up approach for data anonymization using map reduce on cloud -- 2
₹12500-37500 INR
Πληρώθηκε κατά την παράδοση
In big data applications, data privacy is one of the
most concerned issues because processing large-scale
privacy-sensitive data sets often requires computation power
provided by public cloud services. Sub-tree data
anonymization, achieving a good trade-off between data
utility and distortion, is a widely adopted scheme to
anonymize data sets for privacy preservation. Top-Down
Specialization (TDS) and Bottom-Up Generalization (BUG)
are two ways to fulfill sub-tree anonymization. However,
existing approaches for sub-tree anonymization fall short of
parallelization capability, thereby lacking scalability in
handling big data on cloud. Still, both TDS and BUG suffer
from poor performance for certain value of k-anonymity
parameter if they are utilized individually. In this paper, we
propose a hybrid approach that combines TDS and BUG
together for efficient sub-tree anonymization over big data.
Further, we design MapReduce based algorithms for two
components (TDS and BUG) to gain high scalability by
exploiting powerful computation capability of cloud.
Experiment evaluations demonstrate that the hybrid
approach significantly improves the scalability and
efficiency of sub-tree anonymization scheme over existing
approaches.
Ταυτότητα Εργασίας: #7820228
Σχετικά με την εργασία
4 freelancers κάνουν προσφορές κατά μέσο όρο ₹39861 για αυτή τη δουλειά
Hi, I am a java expert. I have rich experiences in java development and maintenance Especially, I have a good experience in mobile app and web based project Send me a private message to discuss details. Reg Περισσότερα
Hello, I am very experienced in Big Data. I also have done some other similar tasks before. I have a firm background and skills as a master of computer science in EPFL, Switzerland (one of top ranked universities Περισσότερα