The Internet of Things (IoT) and cloud technologies have encouraged massive data storage at central repositories. Software-defined networks (SDN) support the processing of data and restrict the transmission of duplicate values. It is necessary to use a data de-duplication mechanism to reduce communication costs and storage overhead. Existing State of the art schemes suffer from computational overhead due to deterministic or random treebased tags generation which further increases as the file size grows. This paper presents an efficient file-level de-duplication scheme (EFDS) where the cost of creating tags is reduced by employing a hash table with keyvalue pair for each block of the file. Further, an algorithm for hash tablebased duplicate block identification and storage (HDBIS) is presented based on fingerprints that maintain a linked list of similar duplicate blocks on the same index. Hash tables normally have a consistent time complexity for lookup, generating, and deleting stored data regardless of the input size. The experiential results show that the proposed EFDS scheme performs better compared to its counterparts.