MrCrypt

Tetali, Sai Deep; Lesani, Mohsen; Majumdar, Rupak; Millstein, Todd

doi:10.1145/2509136.2509554

Cited by 42 publications

(9 citation statements)

References 30 publications

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“…Several approaches exploit partial homomorphic encryption to enable query processing on encrypted data in the cloud (Popa et al 2011;Tetali et al 2013). Others (Arasu et al 2013;Bajaj and Sion 2014) use trusted hardware to process database queries in a protected environment.…”

Section: Related Workmentioning

confidence: 99%

Language-integrated privacy-aware distributed queries

Salvaneschi

Köhler

Sokolowski

et al. 2019

Proc. ACM Program. Lang.

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Distributed query processing is an effective means for processing large amounts of data. To abstract from the technicalities of distributed systems, algorithms for operator placement automatically distribute sequential data queries over the available processing units. However, current algorithms for operator placement focus on performance and ignore privacy concerns that arise when handling sensitive data. We present a new methodology for privacy-aware operator placement that both prevents leakage of sensitive information and improves performance. Crucially, our approach is based on an information-flow type system for data queries to reason about the sensitivity of query subcomputations. Our solution unfolds in two phases. First, placement space reduction generates deployment candidates based on privacy constraints using a syntax-directed transformation driven by the information-flow type system. Second, constraint solving selects the best placement among the candidates based on a cost model that maximizes performance. We verify that our algorithm preserves the sequential behavior of queries and prevents leakage of sensitive data. We implemented the type system and placement algorithm for a new query language SecQL and demonstrate significant performance improvements in benchmarks. CCS Concepts: • Security and privacy → Domain-specific security and privacy architectures; • Computing methodologies → Distributed programming languages.

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Section: Related Workmentioning

confidence: 99%

Language-integrated privacy-aware distributed queries

Salvaneschi

Köhler

Sokolowski

et al. 2019

Proc. ACM Program. Lang.

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“…Tu et al proposed Monomi [101], by improving the performance of CryptDB and increasing the capability to handle a more complex query. Subsequently, several variants of CryptDB have been proposed recently, including MrCrypt [102], Crypsis [103], and Computing on Masked Data (CMD) [104]. MrCrypt [102] -focuses on MapReduce operation.…”

Section: Partial Homomorphic Encryption (Phe)mentioning

confidence: 99%

“…Subsequently, several variants of CryptDB have been proposed recently, including MrCrypt [102], Crypsis [103], and Computing on Masked Data (CMD) [104]. MrCrypt [102] -focuses on MapReduce operation. Likewise, Crypsis [103] focuses on the high-level data flow language, Pig Latin.…”

Section: Partial Homomorphic Encryption (Phe)mentioning

confidence: 99%

“…Likewise, Crypsis [103] focuses on the high-level data flow language, Pig Latin. Similar to CryptDB, the additive homomorphism of both MrCrypt [102] and Crypsis [103] relies on the Pai scheme [47], whereas the EGM [43] scheme is used to support multiplicative homomorphism. On the other hand, CMD [104] further extends CryptDB into NoSQL databases.…”

Section: Partial Homomorphic Encryption (Phe)mentioning

confidence: 99%

“…However, to support different types of queries, every piece of data in CryptDB [100] and its variants [101][102][103][104] need to be encrypted under a different encryption schemes (e.g. Pai encryption scheme [47] is used for supporting count query, Song et al's [10] scheme or EGM [43] scheme are used to support keyword search query, etc.).…”

Section: Partial Homomorphic Encryption (Phe)mentioning

confidence: 99%

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A Survey of Homomorphic Encryption for Outsourced Big Data Computation

Fun¹,

Samsudin²

2016

KSII TIIS

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With traditional data storage solutions becoming too expensive and cumbersome to support Big Data processing, enterprises are now starting to outsource their data requirements to third parties, such as cloud service providers. However, this outsourced initiative introduces a number of security and privacy concerns. In this paper, homomorphic encryption is suggested as a mechanism to protect the confidentiality and privacy of outsourced data, while at the same time allowing third parties to perform computation on encrypted data. This paper also discusses the challenges of Big Data processing protection and highlights its differences from traditional data protection. Existing works on homomorphic encryption are technically reviewed and compared in terms of their encryption scheme, homomorphism classification, algorithm design, noise management, and security assumption. Finally, this paper discusses the current implementation, challenges, and future direction towards a practical homomorphic encryption scheme for securing outsourced Big Data computation.

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