Many mobile and embedded apps possess sensitive data, or secrets. Trusting the operating system (OS), they often keep their secrets in the memory. Recent incidents have shown that the memory is not necessarily secure because the OS can be compromised due to inevitable vulnerabilities resulting from its sheer size and complexity. Existing solutions protect sensitive data against an untrusted OS by running app logic in the Secure world, a Trusted Execution Environment (TEE) supported by the ARM TrustZone technology. Because app logic increases the attack surface of their TEE, these solutions do not work for third-party apps. This work aims to support third-party apps without growing the attack surface, significant development effort, or performance overhead. Our solution, called Ginseng, protects sensitive data by allocating them to registers at compile time and encrypting them at runtime before they enter the memory, due to function calls, exceptions or lack of physical registers. Ginseng does not run any app logic in the TEE and only requires minor markups to support existing apps. We report a prototype implementation based on LLVM, ARM Trusted Firmware (ATF), and the HiKey board. We evaluate it with both microbenchmarks and real-world secret-holding apps. Our evaluation shows Ginseng efficiently protects sensitive data with low engineering effort. For example, a Ginsengenabled web server, Nginx, protects the TLS master key with no measurable overhead. We find Ginseng's overhead is proportional to how often sensitive data in registers have to be encrypted and decrypted, i.e., spilling and restoring sensitive data on a function call or under high register pressure. As a result, Ginseng is most suited to protecting small sensitive data, like a password or social security number.
Time' and 'user event' facts in nondeterministic multimedia presentation make a scheduler hard to determine when multimedia objects are to be rendered. The paper proposes a scheduling algorithm, called Event-based Scheduling (EBS) algorithm which is composed of two steps, preprocessing step and rendering step. In the preprocessing step, all user interaction (event) facts and ail temporal facts of begin condition are converted into 'event + time' fact. After the preprocessing step that sets start condition with 'event + time' fact, event-based table (waiting list) is generated and used to render multimedia objects in rendering step.
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