VISE: Combining Intel SGX and Homomorphic Encryption for Cloud Industrial Control Systems

Abstract

Protecting data-in-use from privileged attackers is challenging. New CPU extensions (notably: Intel SGX ) and cryptographic techniques (specifically: Homomorphic Encryption) can guarantee privacy even in untrusted third-party systems. HE allows sensitive processing on ciphered data. However, it is affected by i) a dramatic ciphertext expansion making HE unusable when bandwidth is narrow, ii) unverifiable conditional variables requiring off-premises support. Intel SGX allows sensitive processing in a secure enclave. Unfortunately, it is i) strictly bonded to the hosting server making SGX unusable when the live migration of cloud VMs/Containers is desirable, ii) limited in terms of usable memory, which is in contrast with resource-consuming data processing. In this paper, we propose the VIrtual Secure Enclave (VISE), an approach that effectively combines the two aforementioned techniques, to overcome their limitations and ultimately make them usable in a typical cloud setup. VISE moves the execution of sensitive HE primitives (e.g., encryption) to the cloud in a remotely attested SGX enclave, and then performs sensitive processing on HE data -outside the enclaveleveraging all the memory resources available. We demonstrate that VISE meets the challenging security and performance requirements of a substantial application in the Industrial Control Systems domain. Our experiments prove the practicability of the proposed solution.

Risk Assessment Driven Use of Advanced SIEM Technology for Cyber Protection of Critical e-Health Processes

“…Our choice was to use the library TFHE [15] since it provides a good compromise between performance penalty and functionalities. Moreover, since it is fully implemented in C, it was fully possible its porting in an SGX environment [16].…”

An Optimization Methodology for Adapting Legacy SGX Applications to Use Switchless Calls

“…The purpose of SGX technology is to provide a secure container, called an enclave, for cloud-native applications. An SGX enclave is protected against access from privileged software (e.g., OS and hypervisor) to leak sensitive data or manipulate control flow, which can be potentially malicious due to the untrustworthy nature of the cloud environment [27][28][29]. Essentially, service providers who run their applications in the cloud platform cannot access or control hardware components and the underlying privileged software components.…”

Privacy-preserving Credit Scoring via Functional Encryption

“…Unfortunately, up to now, industries seldom adopt such advanced cryptography technologies: the majority of cryptography technologies used by industries were developed in early 2000s [14]. Only recently, thanks to the ambitious project FENTEC (Functional ENcryption TEChnologies) 5 , there has been a tentative to propose FE solutions for privacy preserving in a wide range of sectors from clinical data 6 , to public transportation 7 . There are several types of schemes that can be assimilated to the FE, these are: attribute-based encryption (ABE) [10], [3], [13], identity-based encryption (IBE) [4], the ones that implement inner product functions [1], [2] and nonlinear (at most quadratic) polynomials.…”

“…Unfortunately, the exclusive use of HE is not doable since it is affected by a non-negligible execution time overhead, and by a large cipher text expansion. Moreover, a major problem of HE is the so-called unverifiable conditional issue, which forces a program running on a third-party host, and processing homomorphically encrypted data, to request that a client decrypts intermediate functional results to proceed further in the execution [7]. This introduces additional synchronization points and performance overhead, and increases the risk of information disclosure (e.g.…”