Efficient and secure document similarity search cloud utilizing mapreduce
Alewiwi, Mahmoud Khaled (2016) Efficient and secure document similarity search cloud utilizing mapreduce. [Thesis]
Document similarity has important real life applications such as finding duplicate web sites and identifying plagiarism. While the basic techniques such as k-similarity algorithms have been long known, overwhelming amount of data, being collected such as in big data setting, calls for novel algorithms to find highly similar documents in reasonably short amount of time. In particular, pairwise comparison of documents sharing a common feature, necessitates prohibitively high storage and computation power. The wide spread availability of cloud computing provides users easy access to high storage and processing power. Furthermore, outsourcing their data to the cloud guarantees reliability and availability for their data while privacy and security concerns are not always properly addressed. This leads to the problem of protecting the privacy of sensitive data against adversaries including the cloud operator. Generally, traditional document similarity algorithms tend to compare all the documents in a data set sharing same terms (words) with query document. In our work, we propose a new filtering technique that works on plaintext data, which decreases the number of comparisons between the query set and the search set to find highly similar documents. The technique, referred as ZOLIP algorithm, is efficient and scalable, but does not provide security. We also design and implement three secure similarity search algorithms for text documents, namely Secure Sketch Search, Secure Minhash Search and Secure ZOLIP. The first algorithm utilizes locality sensitive hashing techniques and cosine similarity. While the second algorithm uses the Minhash Algorithm, the last one uses the encrypted ZOLIP Signature, which is the secure version of the ZOLIP algorithm. We utilize the Hadoop distributed file system and the MapReduce parallel programming model to scale our techniques to big data setting. Our experimental results on real data show that some of the proposed methods perform better than the previous work in the literature in terms of the number of joins, and therefore, speed.
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