TY - JOUR TI - Finding the duplicated data in cloud storage by using AdjDup Technique AU - Pranay Kumar Katta AU - Yogendra Prasad P JO - International Journal of Scientific Research in Computer Science, Engineering and Information Technology PB - Technoscience Academy DA - 2018/06/30 PY - 2018 DO - https://doi.org/10.32628/IJSRCSEIT UR - https://ijsrcseit.com/CSEIT1835180 VL - 3 IS - 5 SP - 820 EP - 825 AB - Cloud computing greatly facilitates information providers who need to source their information to the cloud while not revealing their sensitive information to external parties and would like users with bound credentials to be ready to access the data.Data reduction has become progressively vital in storage systems due to the explosive growth of digital information within the world that has ushered within the huge information era. one amongst the most challenges facing large-scale information reduction is a way to maximally discover and eliminate redundancy at terribly low overheads. during this paper, we tend to present DARE, a low-overhead Deduplication-Aware resemblance detection and Elimination theme that effectively exploits existing duplicate-adjacency info for extremely economical resemblance detection in information deduplication based mostly backup/archiving storage systems. the most plan behind DARE is to use a theme, decision Duplicate-Adjacency based mostly alikeness Detection (DupAdj), by considering any 2 information chunks to be similar (i.e., candidates for delta compression) if their several adjacent information chunks are duplicate in an exceedingly deduplication system, so more enhance the resemblance detection potency by an improved super-feature approach. Our experimental results supported real-world and artificial backup datasets show that DARE solely consumes regarding 1/4 and 1/2 severally of the computation and assortment overheads needed by the standard super-feature approaches whereas detecting 2-10% a lot of redundancy and achieving a better turnout, by exploiting existing duplicate-adjacency information for resemblance detection and finding the “sweet spot” for the super-feature approach.