SAED: Edge-Based Intelligence for Privacy-Preserving Enterprise Search on the Cloud

Zobaed, SM; Salehi, Mohsen Amini; Buyya, Rajkumar

doi:10.1109/ccgrid51090.2021.00046

Cited by 2 publications

(2 citation statements)

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“…In particular, we study the impact of software architecture (monolithic vs micro-service) used to develop an application on confidential computing. We unfold the structure of monolithic confidential computing applications (e.g., S3C [33], S3BD [34], and ClusPr [35]), as well as microservice-based ones (e.g., SAED [36]). What is more, we study how application partitioning (across edge-cloud) and application-data encryption can influence confidential computing.…”

Section: Taxonomy Of Confidential Computing Across Edge-to-cloudmentioning

confidence: 99%

“…According to the taxonomy, the application logic (i.e., algorithm) can be written such that the sensitive parts are executed on the trusted machine/TEE and the non-sensitive parts are executed on the normal (non-trusted) machines. For instance, SAED [36], a tool for secure semantic search of encrypted cloud data, realizes confidential computing via partitioning the search logic/algorithm. The intelligent part that captures semantics of the user's search query is performed on-premise (on a trusted edge server).…”

Section: Application Architecture and Partitioningmentioning

confidence: 99%

See 1 more Smart Citation

Edge-MultiAI: Multi-Tenancy of Latency-Sensitive Deep Learning Applications on Edge

Zobaed

Mokhtari

Champati

et al. 2022

2022 IEEE/ACM 15th International Conference on Utility and Cloud Computing (UCC)

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Confidential computing has gained prominence due to the escalating volume of datadriven applications (e.g., machine learning and big data) and the acute desire for secure processing of sensitive data, particularly, across distributed environments, such as edge-to-cloud continuum. Provided that the works accomplished in this emerging area are scattered across various research fields, this paper aims at surveying the fundamental concepts, and cutting-edge software and hardware solutions developed for confidential computing using trusted execution environments, homomorphic encryption, and secure enclaves. We underscore the significance of building trust in both hardware and software levels and delve into their applications particularly for machine learning (ML) applications. While substantial progress has been made, there are some barely-explored areas that need extra attention from the researchers and practitioners in the community to improve confidentiality aspects, develop more robust attestation mechanisms, and to address vulnerabilities of the existing trusted execution environments. Providing a comprehensive taxonomy of the confidential computing landscape, this survey enables researchers to advance this field to ultimately ensure the secure processing of users' sensitive data across a multitude of applications and computing tiers.

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