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Providing a method to efficiently search into outsourced encrypted data, without forsaking strong privacy guarantees, is a pressing concern rising from the separation of data ownership and data management typical of cloud-based applications. While several existing solutions allow a client to look-up the occurrences of a substring in an outsourced document collection, the practical application requirements in terms of privacy and efficiency call for the improvement of such solutions. In this work, we present a privacy-preserving substring search protocol with a polylogarithmic communication cost and a limited computational effort on the server side. The proposed protocol provides search pattern and access pattern privacy, for both exact string search, and character-pattern search with wildcards. Its extension to a multi-user setting shows significant savings in terms of outsourced storage w.r.t. a baseline solution where the whole dataset is replicated. The performance figures of an optimized implementation of our protocol, searching into a remotely stored genomic dataset, validate the practicality of the approach exhibiting a data transfer of less than 50 kiB to execute a query over a document of 40 MiB, with execution times on client and server in the range of a few seconds and a few minutes, respectively.
Providing a method to efficiently search into outsourced encrypted data, without forsaking strong privacy guarantees, is a pressing concern rising from the separation of data ownership and data management typical of cloud-based applications. While several existing solutions allow a client to look-up the occurrences of a substring in an outsourced document collection, the practical application requirements in terms of privacy and efficiency call for the improvement of such solutions. In this work, we present a privacy-preserving substring search protocol with a polylogarithmic communication cost and a limited computational effort on the server side. The proposed protocol provides search pattern and access pattern privacy, for both exact string search, and character-pattern search with wildcards. Its extension to a multi-user setting shows significant savings in terms of outsourced storage w.r.t. a baseline solution where the whole dataset is replicated. The performance figures of an optimized implementation of our protocol, searching into a remotely stored genomic dataset, validate the practicality of the approach exhibiting a data transfer of less than 50 kiB to execute a query over a document of 40 MiB, with execution times on client and server in the range of a few seconds and a few minutes, respectively.
Data confidentiality is a central concern in modern computer systems and services, as sensitive data from users and companies are being increasingly delegated to such systems. Several hardware-based mechanisms have been recently proposed to enforce security guarantees of sensitive information. Hardware-based isolated execution environments are a class of such mechanisms, in which the operating system and other low-level components are removed from the trusted computing base. One of such mechanisms is the Intel Software Guard Extensions (Intel SGX), which creates the concept of enclave to encapsulate sensitive components of applications and their data. Despite being largely applied in several computing areas, SGX has limitations and performance issues that must be addressed for the development of secure solutions. This text brings a categorized literature review of the ongoing research on the Intel SGX architecture, discussing its applications and providing a classification of the solutions that take advantage of SGX mechanisms. We analyze and categorize 293 papers that rely to SGX to provide integrity, confidentiality, and privacy to users and data, regarding different contexts and goals. We also discuss research challenges and provide future directions in the field of enclaved execution, particularly when using SGX.
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