Privacy-preserving multi-keyword ranked search over encrypted cloud data

By Ning Cao, Cong Wang, Ming Li, Kui Ren, Wenjing Lou. 2011 Proceedings IEEE INFOCOM, 2011

1,232 citations recorded by the source corpus.

With the advent of cloud computing, data owners are motivated to outsource their complex data management systems from local sites to the commercial public cloud for great flexibility and economic savings. But for protecting data privacy, sensitive data has to be encrypted before outsourcing, which obsoletes traditional data utilization based on plaintext keyword search. Thus, enabling an encrypted cloud data search service is of paramount importance. Considering the large number of data users and documents in the cloud, it is necessary to allow multiple keywords in the search request and return documents in the order of their relevance to these keywords. Related works on searchable encryption focus on single keyword search or Boolean keyword search, and rarely sort the search results. In this paper, for the first time, we define and solve the challenging problem of privacy-preserving multi-keyword ranked search over encrypted cloud data (MRSE).We establish a set of strict privacy requirements for such a secure cloud data utilization system. Among various multi-keyword semantics, we choose the efficient similarity measure of “coordinate matching”, i.e., as many matches as possible, to capture the relevance of data documents to the search query. We further use “inner product similarity” to quantitatively evaluate such similarity measure. We first propose a basic idea for the MRSE based on secure inner product computation, and then give two significantly improved MRSE schemes to achieve various stringent privacy requirements in two different threat models. Thorough analysis investigating privacy and efficiency guarantees of proposed schemes is given. Experiments on the real-world dataset further show proposed schemes indeed introduce low overhead on computation and communication.

Subfields: Artificial Intelligence, Information Systems

View Paper: https://www.semanticscholar.org/paper/d7ccf8266caa85ec8d65ef828d21f1c161466427

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Kui Ren
2. Wenjing Lou
3. Ming Li
4. Xingming Sun
5. Ning Cao
6. Shucheng Yu
7. Xinhui Wang
8. Zhihua Xia

In Information Systems

1. Kui Ren
2. Wenjing Lou
3. Ming Li
4. Ning Cao
5. Xingming Sun
6. Xinhui Wang
7. Zhihua Xia
8. Hui Li

Citations in the corpus

• The rise of "big data" on cloud computing: Review and open research issues (2015, methodology): “Currently, encryption is utilized by most researchers to ensure data privacy in the cloud [107,108].”
• Privacy-preserving multi-keyword fuzzy search over encrypted data in the cloud (2014, background): “In our design, we would like to eliminate this requirement in contrast to many previous solutions [2], [4], [8]–[10], [12].”
• Security and Privacy Issues of Fog Computing: A Survey (2015, background): “Privacy-preserving techniques have been proposed in many scenarios including cloud [4], smart grid [28], wireless network [27] , and online social network [24].”
• Enabling Personalized Search over Encrypted Outsourced Data with Efficiency Improvement (2016, methodology): “We propose a basic design of PRSE, and then give two PRSE schemes based on secure inner product [14] in order to meet different search intentions.”
• Toward Efficient Multi-Keyword Fuzzy Search Over Encrypted Outsourced Data With Accuracy Improvement (2016, methodology): “[2] proposed a privacy-preserving multi-keyword ranked search scheme which used coordinate matching to realize ranked search.”
• An efficient public-key searchable encryption scheme secure against inside keyword guessing attacks (2017, background): “Roughly speaking, these works can be classified into the following types: 1) multi-users access control in PEKS [37, 31, 38], 2) fuzzy keyword search in PEKS [10, 12, 27], 3) flexible keyword search in PEKS [29, 18, 24], and 4) trapdoor privacy in PEKS [42, 23, 1, 28, 35].”
• Data Security and Privacy Protection for Cloud Storage: A Survey (2020, background): “Cao et al. [18] proposed a secure multi-keyword ranked search over encrypted data.”
• Privacy-Preserving Multi-Keyword Ranked Search over Encrypted Cloud Data (2014, methodology): “Our early work [1] has been aware of this problem, and provides solutions to the multi-keyword ranked search over encrypted data problem.”
• Achieving usable and privacy-assured similarity search over outsourced cloud data (2012, background): “Very recently, secure ranked search over outsourced cloud data, which further facilitates search result relevance ranking, has been studied in the settings of single keyword [22] and multi-keyword search [23], respectively.”
• Data Security and Privacy in Cloud Computing (2014, neutral): “A privacy-preserving multikeyword ranked search approach over encrypted cloud data was proposed [29], which can search the encrypted cloud data and rank the search results without leakage of the user’s privacy.”
• A Survey of Fog Computing: Concepts, Applications and Issues (2015, background): “Privacy-preserving techniques have been proposed in many scenarios including cloud [48, 4], smart grid [40], wireless network [39] , and online social network [35].”
• A Privacy-Preserving and Copy-Deterrence Content-Based Image Retrieval Scheme in Cloud Computing (2016, methodology): “In addition, just as the previous SSE schemes [2]–[4], [6]–[9], [12]–[14], [22]–[27], [33], it is easy to deduce that the images m i and m j are similar to each other if both the images m i and m j have the high similarity scores to the same query image.”
• A Secure and Dynamic Multi-Keyword Ranked Search Scheme over Encrypted Cloud Data (2016, methodology): “Due to the special structure of our tree-based index, the proposed search scheme can flexibly achieve sublinear search time and deal with the deletion and insertion of documents.”
• Verifiable Privacy-Preserving Multi-Keyword Text Search in the Cloud Supporting Similarity-Based Ranking (2013, methodology): “We assume that the cloud server acts in an ‘‘honest-butcurious’’ manner, which is also employed by related works on secure cloud data search [9], [21].”
• Enabling Secure and Efficient Ranked Keyword Search over Outsourced Cloud Data (2012, background): “[30] propose a privacy-preserving multikeyword ranked search scheme, which extends our previous work in [1] with support of multikeyword query.”