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We study the problem of publishing a stream of real-valued data satisfying differential privacy (DP). One major challenge is that the maximal possible value in the stream can be quite large, leading to enormous DP noise and bad utility. To reduce the maximal value and noise, one way is to estimate a threshold so that values above it can be truncated. The intuition is that, in many scenarios, only a few values are large; thus truncation does not change the original data much. We develop such a method that finds a suitable threshold with DP. Given the threshold, we then propose an online hierarchical method and several post-processing techniques. Building on these ideas, we formalize the steps in a framework for the private publishing of streaming data. Our framework consists of three components: a threshold optimizer that privately estimates the threshold, a perturber that adds calibrated noise to the stream, and a smoother that improves the result using post-processing. Within our framework, we also design an algorithm satisfying the more stringent DP setting called local DP. Using four real-world datasets, we demonstrate that our mechanism outperforms the state-of-the-art by a factor of 6−10 orders of magnitude in terms of utility (measured by the mean squared error of the typical scenario of answering a random range query). 

Intel SGX is a hardware-based trusted execution environment (TEE), which enables an application to compute on confidential data in a secure enclave. SGX assumes a powerful threat model, in which only the CPU itself is trusted; anything else is untrusted, including the memory, firmware, system software, etc. An enclave interacts with its host application through an exposed, enclave-specific, (usually) bi-directional interface. This interface is the main attack surface of the enclave. The attacker can invoke the interface in any order and inputs. It is thus imperative to secure it through careful design and defensive programming. In this work, we systematically analyze the attack models against the enclave untrusted interfaces and summarized them into the COIN attacks -- Concurrent, Order, Inputs, and Nested. Together, these four models allow the attacker to invoke the enclave interface in any order with arbitrary inputs, including from multiple threads. We then build an extensible framework to test an enclave in the presence of COIN attacks with instruction emulation and concolic execution. We evaluated ten popular open-source SGX projects using eight vulnerability detection policies that cover information leaks, control-flow hijackings, and memory vulnerabilities. We found 52 vulnerabilities. In one case, we discovered an information leak that could reliably dump the entire enclave memory by manipulating the inputs. Our evaluation highlights the necessity of extensively testing an enclave before its deployment. 

Recent advances in trusted execution environments, specifically with Intel's introduction of SGX on consumer processors, have provided unprecedented opportunities to create secure applications with a small TCB. While a large number of SGX solutions have been proposed, nearly all of them focus on protecting native code applications, leaving scripting languages unprotected. To fill this gap, this paper presents SCRIPTSHIELD, a framework capable of running legacy script code while simultaneously providing confidentiality and integrity for scripting code and data. In contrast to the existing schemes that either require tedious and time-consuming re-development or result in a large TCB by importing an entire library OS or container, SCRIPTSHIELD keeps the TCB small and provides backwards compatibility (i.e., no changes needed to the scripting code itself). The core idea is to customize the script interpreter to run inside an SGX enclave and pass scripts to it. We have implemented SCRIPTSHIELD and tested with three popular scripting languages: Lua, JavaScript, and Squirrel. Our experimental results show that SCRIPTSHIELD does not cause noticeable overhead. The source code of SCRIPTSHIELD has been made publicly available as an open source project.