RESeED: A secure regular‐expression search tool for storage clouds

Abstract

Summary Lack of trust has become one of the main concerns of users who tend to utilize one or multiple Cloud providers. Trustworthy Cloud‐based computing and data storage require secure and efficient solutions which allow clients to remotely store and process their data in the Cloud. User‐side encryption is an established method to secure the user data on the Cloud. However, using encryption, we lose processing capabilities, such as searching, over the Cloud data. In this paper, we present RESeED, a tool that provides user‐transparent and Cloud‐agnostic regular‐expression search functionality over encrypted data across multiple Clouds. Upon a client's intent to upload a new document to the Cloud, RESeED analyzes the document's content and updates its data structures accordingly. Then, it encrypts and transfers the document to the Cloud. RESeED provides the regular‐expression search functionality over encrypted data by translating the search queries on‐the‐fly to finite automata and analyzing concise and secure representations of the data before asking the Cloud to download the encrypted documents. RESeED's parallel architecture enables efficient search over large‐scale (and potentially big data scale) data‐sets. We evaluate the performance of RESeED experimentally and demonstrate its scalability and correctness using real‐world data‐sets from arXiv.org and Internet Engineering Task Force (IETF). Our results show that RESeED produces accurate query responses with a reasonable (≃6%) storage overhead. The results also demonstrate that for many search queries, RESeED performs faster in compare with the grep utility that functions on unencrypted data. Copyright © 2017 John Wiley & Sons, Ltd.

Formal Framework for Safety, Security, and Availability of Aircraft Communication Networks

“…Galois-field-based syndromes to improve fault-tolerance; PEKS-like [11,16] regular expression based keywords and meta-keywords for privacy-preserving search [85,86]; and a dynamically-sized payload as pictured in Fig. 7.…”

“…Each data object transmits with a further modification to add encryption using current standards for end-toend encryption, such as ECCDHE [87,88]. Trap-door encrypted keywords included with the transmission enable intermediaries to route and index information securely using the RESeED framework for regular-expression based search over encrypted data [85,86]. This allows the data to be transmitted in a semi-oblivious manner.…”

Survey on secure search over encrypted data on the cloud

“…Sun et al 14 35-37,40,48 49,50 N/A Wang et al 35,36 Regular Expression Search N/A N/A N/A Salehi et al 20,51 Other works 11,13,25,32,36,37 Semantic Search Other works 14,18,28,35,54 Other works 14,18,28,35 Woodworth et al 28 Other works 26,28,54 Other works 14,15,21,28 of the two schemes called WBSKS. In the SBKS scheme, alongside the process of extracting important keywords of the document, synonyms that represent the semantic of a document are collected.…”

ClustCrypt: Privacy-Preserving Clustering of Unstructured Big Data in the Cloud

“…For instance, in S3C [35] search tool, for a 100-petabyte dataset the index size grows to ≈ 300 GB. Traversing such a large index affects searching timeliness and can be potentially impacted by hardware limitations [32]. To resolve the bottleneck, the index structure can be partitioned to several clusters [12], [36].…”

“…For a given search query, instead of searching the whole index, the search space is pruned and gets limited to only those clusters that are topically related to the search query. The pruning is achieved based on a set of Abstract structures (denoted a 1 , ..., a n ) that are sampled from each one of the clusters and reside either on the client tier or possibly on a trusted edge server [32]. Details of the way abstracts are created described in [21] [11].…”