Order-preserving minimal perfect hash functions and information retrieval

Fox, Edward A.; Chen, Qi Fan; Daoud, Amjad M.; Heath, Lenwood S.

doi:10.1145/125187.125200

Cited by 47 publications

(33 citation statements)

References 5 publications

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“…Related work also includes research on order-preserving hashing [5] [11]. However, protecting the hash values from cryptanalysis is not the concern of this body of work.…”

Section: Related Workmentioning

confidence: 99%

Order preserving encryption for numeric data

Agrawal

Kiernan

Srikant

et al. 2004

Proceedings of the 2004 ACM SIGMOD International Conference on Management of Data

974

775

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Encryption is a well established technology for protecting sensitive data. However, once encrypted, data can no longer be easily queried aside from exact matches. We present an order-preserving encryption scheme for numeric data that allows any comparison operation to be directly applied on encrypted data. Query results produced are sound (no false hits) and complete (no false drops). Our scheme handles updates gracefully and new values can be added without requiring changes in the encryption of other values. It allows standard database indexes to be built over encrypted tables and can easily be integrated with existing database systems. The proposed scheme has been designed to be deployed in application environments in which the intruder can get access to the encrypted database, but does not have prior domain information such as the distribution of values and cannot encrypt or decrypt arbitrary values of his choice. The encryption is robust against estimation of the true value in such environments.

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“…Related work also includes research on order-preserving hashing [5] [11]. However, protecting the hash values from cryptanalysis is not the concern of this body of work.…”

Section: Related Workmentioning

confidence: 99%

Order preserving encryption for numeric data

Agrawal

Kiernan

Srikant

et al. 2004

Proceedings of the 2004 ACM SIGMOD International Conference on Management of Data

974

775

View full text Add to dashboard Cite

show abstract

“…We make this assumption without loss of generality because any value can be transformed to a real number using a hash function. For example, in the case of a string value, the order-preserving hash functions proposed by Fox et al [8] can be exploited.…”

Section: The Form Of Data Streamsmentioning

confidence: 99%

Continuous query processing in data streams using duality of data and queries

Lim

Lee

et al. 2006

Proceedings of the 2006 ACM SIGMOD International Conference on Management of Data

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Recent data stream systems such as TelegraphCQ have employed the well-known property of duality between data and queries. In these systems, query processing methods are classified into two dual categories -data-initiative and query-initiative -depending on whether query processing is initiated by selecting a data element or a query. Although the duality property has been widely recognized, previous data stream systems do not fully take advantages of this property since they use the two dual methods independently: data-initiative methods only for continuous queries and query-initiative methods only for ad-hoc queries. We contend that continuous query processing can be better optimized by adopting an approach that integrates the two dual methods. Our primary contribution is based on the observation that spatial join is a powerful tool for achieving this objective. In this paper, we first present a new viewpoint of transforming the continuous query processing problem to a multi-dimensional spatial join problem. We then present a continuous query processing algorithm based on spatial join, which we name Spatial Join CQ. This algorithm processes continuous queries by finding the pairs of overlapping regions from a set of data elements and a set of queries, both defined as regions in the multi-dimensional space. The algorithm achieves the advantages of the two dual methods simultaneously. Experimental results show that the proposed algorithm outperforms earlier algorithms by up to 36 times for simple selection continuous queries and by up to 7 times for sliding window join queries.

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“…This is the environment that we consider here. [6,7]; this allows the vocabulary to remain compact (indeed, the words need not be stored at all), but a non-trivial amount of memory space is required for the description of the hash function, approximately 2-4 bits per key, and two disk accesses will still be required per query term if the address table is stored on disk.…”

Section: Document Databasesmentioning

confidence: 99%