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—Intel SGX, a new security capability in emerging CPUs, allows user-level application code to execute in hardware-isolated enclaves. Enclave memory is isolated from all other software on the system, even from the privileged OS or hypervi-sor. While being a promising hardware-rooted building block, enclaves have severely limited capabilities, such as no(More)
Web servers are vulnerable to a large class of attacks which can allow network attacker to steal sensitive web content. In this work, we investigate the feasibility of a web server architecture, wherein the vulnerable server VM runs on a trusted cloud. All sensitive web content is made available to the vulnerable server VM in encrypted form, thereby(More)
Web browsers isolate web origins, but do not provide direct abstractions to isolate sensitive data and control computation over it within the same origin. As a result, guaranteeing security of sensitive web content requires trusting all code in the browser and client-side applications to be vulnerability-free. In this paper, we propose a new abstraction,(More)
—Secure execution of applications on untrusted operating systems is a fundamental security primitive that has been challenging to achieve. In this paper, we propose a new architecture feature called PODARCH, which makes it easy to import executables on an OS without risking the target system's security or the execution of the imported application. PODARCH(More)
Peer-to-peer (P2P) systems are predominantly used to distribute trust, increase availability and improve performance. A number of content-sharing P2P systems, for file-sharing applications (e.g., BitTorrent and Storj) and more recent peer-assisted CDNs (e.g., Akamai Netses-sion), are finding wide deployment. A major security concern with content-sharing P2P(More)
Secure computation on encrypted data stored on untrusted clouds is an important goal. Existing secure arithmetic computation techniques, such as fully homomorphic encryption (FHE) and somewhat homomorphic encryption (SWH), have prohibitive performance and/or storage costs for the majority of practical applications. In this work, we investigate a new secure(More)
Privacy preserving computation is gaining importance. Along with secure computation guarantees, it is essential to hide information leakage through access patterns. Input-oblivious execution is a security property that is crucial to guarantee complete privacy preserving computation. In this work, we present an algorithm-specific approach to achieve(More)
Deep learning in a collaborative setting is emerging as a cornerstone of many upcoming applications, wherein untrusted users collaborate to generate more accurate models. From the security perspective , this opens collaborative deep learning to poisoning attacks , wherein adversarial users deliberately alter their inputs to mis-train the model. These(More)