Searchable Encryption over Feature-Rich Data

Qian Wang

doi:10.1109/TDSC.2016.2593444

2018-05-01

144

PRE

DOI：10.1109/TDSC.2016.2593444

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摘要

En 中文

Storage services allow data owners to store their huge amount of potentially sensitive data, such as audios, images, and videos, on remote cloud servers in encrypted form. To enable retrieval of encrypted files of interest, searchable symmetric encryption (SSE) schemes have been proposed. However, many schemes construct indexes based on keyword-file pairs and focus on boolean expressions of exact keyword matches. Moreover, most dynamic SSE schemes cannot achieve forward privacy and reveal unnecessary information when updating the encrypted databases. We tackle the challenge of supporting large-scale similarity search over encrypted feature-rich multimedia data, by considering the search criteria as a high-dimensional feature vector instead of a keyword. Our solutions are built on carefully-designed fuzzy Bloom filters which utilize locality sensitive hashing (LSH) to encode an index associating the file identifiers and feature vectors. Our schemes are proven to be secure against adaptively chosen query attack and forward private in the standard model. We have evaluated the performance of our scheme on real-world high-dimensional datasets, and achieved a search quality of 99 percent recall with only a few number of hash tables for LSH. This shows that our index is compact and searching is not only efficient but also accurate.

Keyword:

Cloud storage

searchable encryption

homomorphic encryption

similarity search

proximity search

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期刊

IEEE Transactions on Dependable and Secure Computing

IF:

7.5

论文数:

2.4K

被引数:

9.6K

机构

University of Hong Kong

学者数:

4.1W

论文数: 3.9W

被引数: 10.1W

Chinese University of Hong Kong

学者数:

3.4W

论文数: 3.2W

被引数: 5.6W

wuhan university

学者数:

8.1W

论文数: 5.8W

被引数: 70

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