TrustAV

Abstract

While the number of connected devices is constantly growing, we observe an increased incident rate of cyber attacks that target user data. Typically, personal devices contain the most sensitive information regarding their users, so there is no doubt that they can be a very valuable target for adversaries. Typical defense solution to safeguard user devices and data, are based in malware analysis mechanisms. To amortize the processing and maintenance overheads, the outsourcing of network inspection mechanisms to the cloud has become very popular recently. However, the majority of such cloud-based applications usually offers limited privacy preserving guarantees for data processing in third-party environments.In this work, we propose TrustAV, a practical cloud-based malware detection solution destined for a plethora of device types. TrustAV is able to offload the processing of malware analysis to a remote server, where it is executed entirely inside, hardware supported, secure enclaves. By doing so, TrustAV is capable to shield the transfer and processing of user data even in untrusted environments with tolerable performance overheads, ensuring that private user data are never exposed to malicious entities or honestbut-curious providers. TrustAV also utilizes various techniques in order to overcome performance overheads, introduced by the Intel SGX technology, and reduce the required enclave memory -a limiting factor for malware analysis executed in secure enclave environments-offering up to 3x better performance. CCS CONCEPTS• Security and privacy → Systems security; Intrusion detection systems; • Networks → Cloud computing.

SoK: Content Moderation for End-to-End Encryption

“…Matching via general crypto or MPC [80,81,169,205,296,303,333,349] (total: 9) Client-side or metadata-based [177,211,366,374] (total: 4) AI via MPC or federated learning [64,122,333,334] (total: 4) Matching in Trusted Execution Environment [95,350,372] (total: 3)…”