Scaling Cryptographic Techniques by Exploiting Data Sensitivity at a Public Cloud

Abstract

Despite extensive research on cryptography, secure and efficient query processing over outsourced data remains an open challenge. This poster continues along the emerging trend in secure data processing that recognizes that the entire dataset may not be sensitive, and hence, non-sensitivity of data can be exploited to overcome some of the limitations of existing encryption-based approaches. In particular, this poster outlines a new secure keyword search approach, called query keyword binning (QB) that allows non-sensitive parts of the data to be outsourced in clear-text while guaranteeing that no information is leaked by joint processing of non-sensitive data (in clear-text) and sensitive data (in encrypted form). QB improves the performance of and strengthens the security of the underlying cryptographic technique by preventing size, frequency-count, and workload-skew attacks.