IntelSGXhas started to be widely adopted. Cloud providers (Microsoft Azure, IBM Cloud, Alibaba Cloud) are offering new solutions, implementingdata-in-useprotection via SGX. A major challenge faced by both academia and industry is providing transparent SGX support to legacy applications. The approach with the highest consensus is linking the target software with SGX-extendedlibclibraries. Unfortunately, the increased security entails a dramatic performance penalty, which is mainly due to the intrinsic overhead of context switches, and the limited size of protected memory. Performance optimization is non-trivial since it depends on key parameters whose manual tuning is a very long process. We present the architecture of an automated tool, calledSGXTuner, which is able to find the best setting of SGX-extendedlibclibrary parameters, by iteratively adjusting such parameters based on continuous monitoring of performance data. The tool is — to a large extent — algorithm agnostic. We decided to base the current implementation on a particular type of stochastic optimization algorithm, specificallySimulated Annealing. A massive experimental campaign was conducted on a relevant case study. Three client-server applications —Memcached,Redis, andApache— were compiled with SCONE'ssgx-musland tuned for best performance. Results demonstrate the effectiveness ofSGXTuner.

Trust is arguably the most important challenge for critical services both deployed as well as accessed remotely over the network. These systems are exposed to a wide diversity of threats, ranging from bugs to exploits, active attacks, rogue operators, or simply careless administrators. To protect such applications, one needs to guarantee that they are properly configured and securely provisioned with the "secrets" (e.g., encryption keys) necessary to preserve not only the confidentiality, integrity and freshness of their data but also their code. Furthermore, these secrets should not be kept under the control of a single stakeholder—which might be compromised and would represent a single point of failure—and they must be protected across software versions in the sense that attackers cannot get access to them via malicious updates. Traditional approaches for solving these challenges often use ad hoc techniques and ultimately rely on a hardware security module (HSM) as root of trust. We propose a more powerful and generic approach to trust management that instead relies on trusted execution environments (TEEs) and a set of stakeholders as root of trust. Our system, PALÆMON, can operate as a managed service deployed in an untrusted environment, i.e., one can delegate its operations to an untrusted cloud provider with the guarantee that data will remain confidential despite not trusting any individual human (even with root access) nor system software. PALÆMON addresses in a secure, efficient and cost-effective way five main challenges faced when developing trusted networked applications and services. Our evaluation on a range of benchmarks and real applications shows that PALÆMON performs efficiently and can protect secrets of services without any change to their source code.

Data-driven intelligent applications in modern online services have become ubiquitous. These applications are usually hosted in the untrusted cloud computing infrastructure. This poses significant security risks since these applications rely on applying machine learning algorithms on large datasets which may contain private and sensitive information.

This paper presents PUBSUB-SGX, a content-based publish-subscribe system that exploits trusted execution environments (TEEs), such as Intel SGX, to guarantee confidentiality and integrity of data as well as anonymity and privacy of publishers and subscribers. We describe the technical details of our Python implementation, as well as the required system support introduced to deploy our system in a container-based runtime. Our evaluation results show that our approach is sound, while at the same time highlighting the performance and scalability trade-offs. In particular, by supporting just-in-time compilation inside of TEEs, Python programs inside of TEEs are in general faster than when executed natively using standard CPython.

Shielded execution based on Intel SGX provides strong security guarantees for legacy applications running on untrusted platforms. However, memory safety attacks such as Heartbleed can render the confidentiality and integrity properties of shielded execution completely ineffective. To prevent these attacks, the state-of-the-art memory-safety approaches can be used in the context of shielded execution.

With the spreading of multi-core architectures, operating systems and applications are becoming increasingly more concurrent and their scalability is often limited by the primitives used to synchronize the different hardware threads. In this paper, we address the problem of how to optimize the throughput of a system with multiple producer and consumer threads. Such applications typically synchronize their threads via multi-producer/multi-consumer FIFO queues, but existing solutions have poor scalability, as we could observe when designing a secure application framework that requires high-throughput communication between many concurrent threads. In our target system, however, the items enqueued by different producers do not necessarily need to be FIFO ordered. Hence, we propose a fast FIFO queue, FFQ, that aims at maximizing throughput by specializing the algorithm for single-producer/multiple-consumer settings: each producer has its own queue from which multiple consumers can concurrently dequeue. Furthermore, while we provide a wait-free interface for producers, we limit ourselves to lock-free consumers to eliminate the need for helping. We also propose a multi-producer variant to show which synchronization operations we were able to remove by focusing on a single producer variant. Our evaluation analyses the performance using micro-benchmarks and compares our results with other state-of-the-art solutions: FFQ exhibits excellent performance and scalability.

Many modern embedded systems use networks to communicate. This increases the attack surface: the adversary does not need to have physical access to the system and can launch remote attacks. By exploiting software bugs, the attacker might be able to change the behavior of a program. Security violations in safety-critical systems are particularly dangerous since they might lead to catastrophic results. Hence, safety-critical software requires additional protection. We present an approach to detect and prevent control flow attacks. Such attacks maliciously modify program's control flow to achieve the desired behavior. We develop ControlFreak, a hardware watchdog to monitor program execution and to prevent illegal control flow transitions. The watchdog employs chained signatures to detect any modification of the instruction stream and any illegal jump in the program even if signatures are maliciously modified.

Every somewhat complex computer system contains bugs. As it is nearly impossible to fix all bugs in the software stack, the only alternative remains is to make the system secure accepting the fact that software is vulnerable. In this work, a hardware monitor is proposed that checks the correctness of program execution using chained signatures.