Multi-Dimensional Range Query over Encrypted Data

By Elaine Shi, John Bethencourt, T-H. Hubert Chan, Dawn Song, Adrian Perrig. IEEE Symposium on Security and Privacy, 2007

570 citations recorded by the source corpus.

Subfields: Artificial Intelligence

View Paper: https://www.semanticscholar.org/paper/20398dd7b439f38b611389d41ee52c0f1a9d6367

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. Brent Waters
4. Ning Cao
5. Ming Li
6. Amit Sahai
7. Dan Boneh
8. Allison Bishop

Citations in the corpus

• Cryptographic Cloud Storage (2010, background): “Several works have shown how to achieve more complex search queries in the public-key setting, including conjunctive searches [28,13] and range queries [13, 31 ].”
• Parallel and Dynamic Searchable Symmetric Encryption (2013, background): “This includes conjunctive queries in the symmetric key setting [10]; it also includes conjunctive queries [16,2], comparison and subset queries [2], and range queries [17] in the public-key setting.”
• Order-Preserving Symmetric Encryption (2009, background): “Allowing range queries on encrypted data in the public-key setting was studied in [11,26].”
• Controlling data in the cloud: outsourcing computation without outsourcing control (2009, background): “For example, searchable encryption (also referred to as predicate encryption; see [43], [9], [42], [41], and [10]) allows the data owner to compute a capability from his secret key.”
• Order-Preserving Encryption Revisited: Improved Security Analysis and Alternative Solutions (2011, background): “Allowing range queries on encrypted data in the public-key setting was studied in [11, 19], but the solutions are not suitable for large databases, requiring to scan the whole database on every query.”
• Authorized Private Keyword Search over Encrypted Data in Cloud Computing (2011, background): “There are only a few works that specifically tackle this challenging problem, such as [ 33 ].”
• Enabling Secure and Efficient Ranked Keyword Search over Outsourced Cloud Data (2012, background): “Allowing range queries over encrypted data in the public key settings has been studied in [31], [32], where advanced privacy-preserving schemes were proposed to allow more sophisticated multiattribute search over encrypted files while preserving the attributes’ privacy.”
• Privacy-Preserving Queries over Relational Databases (2010, background): “Information-theoretic privacy ensures that the adversary cannot learn the user’s query, regardless of its current or future computational abilities.”
• A Survey of Provably Secure Searchable Encryption (2014, methodology): “…of encryption that covers various cryptographic primitives such as identity-based encryption (IBE) [Shamir 1985; Boneh and Franklin 2001, 2003; Cocks 2001], hiddenvector encryption (HVE) [Boyen and Waters 2006; Shi et al. 2007], and attributebased encryption (ABE) [Sahai and Waters 2005].”
• TACKing Together Efficient Authentication, Revocation, and Privacy in VANETs (2009, background): “Two recent works estimate the performance of running type A pairings on a modern workstation and ECDSA on a memory-constrained 400MHz machine [29, 35].”
• Predicate Encryption Supporting Disjunctions, Polynomial Equations, and Inner Products (2008, background): “Shi et al. [ 22 ] also construct a scheme for range queries, but prove security in a weaker model where attribute hiding is required to hold only if the adversary holds keys that do not allow recovery of the message.”
• Searchable symmetric encryption: Improved definitions and efficient constructions (2006, background): “This includes conjunctive queries in the symmetric key setting [26, 6]; it also includes conjunctive queries [37, 17], comparison and subset queries [17], and range queries [39] in the public-key setting.”
• CryptDB: protecting confidentiality with encrypted query processing (2011, background): “Additional operations can be added to CryptDB by extending its existing onions, or adding new onions for specific data types (e.g., spatial and multi-dimensional range queries [43]).”