Confidentiality, integrity protection, and high availability, abbreviated to CIA, are essential properties for trustworthy data systems. The rise of cloud computing and the growing demand for multiparty applications however means that building modern CIA systems is more challenging than ever. In response, we present the Confidential Consortium Framework (CCF), a general-purpose foundation for developing secure stateful CIA applications. CCF combines centralized compute with decentralized trust, supporting deployment on untrusted cloud infrastructure and transparent governance by mutually untrusted parties. CCF leverages hardware-based trusted execution environments for remotely verifiable confidentiality and code integrity. This is coupled with state machine replication backed by an auditable immutable ledger for data integrity and high availability. CCF enables each service to bring its own application logic, custom multiparty governance model, and deployment scenario, decoupling the operators of nodes from the consortium that governs them. CCF is open-source and available now at https://github.com/microsoft/CCF.

Advances in graphics and machine learning have led to the general availability of easy-to-use tools for modifying and synthesizing media. The proliferation of these tools threatens to cast doubt on the veracity of all media. One approach to thwarting the flow of fake media is to detect modified or synthesized media through machine learning methods. While detection may help in the short term, we believe that it is destined to fail as the quality of fake media generation continues to improve. Soon, neither humans nor algorithms will be able to reliably distinguish fake versus real content. Thus, pipelines for assuring the source and integrity of media will be required---and increasingly relied upon. We present AMP, a system that ensures the authentication of media via certifying provenance. AMP creates one or more publisher-signed manifests for a media instance uploaded by a content provider. These manifests are stored in a database allowing fast lookup from applications such as browsers. For reference, the manifests are also registered and signed by a permissioned ledger, implemented using the Confidential Consortium Framework (CCF). CCF employs both software and hardware techniques to ensure the integrity and transparency of all registered manifests. AMP, through its use of CCF, enables a consortium of media providers to govern the service while making all its operations auditable. The authenticity of the media can be communicated to the user via visual elements in the browser, indicating that an AMP manifest has been successfully located and verified.

We propose EnclaveDB, a database engine that guarantees confidentiality, integrity, and freshness for data and queries. EnclaveDB guarantees these properties even when the database administrator is malicious, when an attacker has compromised the operating system or the hypervisor, and when the database runs in an untrusted host in the cloud. EnclaveDB achieves this by placing sensitive data (tables, indexes and other metadata) in enclaves protected by trusted hardware (such as Intel SGX). EnclaveDB has a small trusted computing base, which includes an in-memory storage and query engine, a transaction manager and pre-compiled stored procedures. A key component of EnclaveDB is an efficient protocol for checking integrity and freshness of the database log. The protocol supports concurrent, asynchronous appends and truncation, and requires minimal synchronization between threads. Our experiments using standard database benchmarks and a performance model that simulates large enclaves show that EnclaveDB achieves strong security with low overhead (up to 40% for TPC-C) compared to an industry strength in-memory database engine.

Safe programming languages are readily available, but many applications continue to be written in unsafe languages because of efficiency. As a consequence, many applications continue to have exploitable memory safety bugs. Since garbage collection is a major source of inefficiency in the implementation of safe languages, replacing it with safe manual memory management would be an important step towards solving this problem.

Garbage collection greatly improves programmer productivity and ensures memory safety. Manual memory management on the other hand often delivers better performance but is typically unsafe and can lead to system crashes or security vulnerabilities. We propose integrating safe manual memory management with garbage collection in the .NET runtime to get the best of both worlds. In our design, programmers can choose between allocating objects in the garbage collected heap or the manual heap. All existing applications run unmodified, and without any performance degradation, using the garbage collected heap. Our programming model for manual memory management is flexible: although objects in the manual heap can have a single owning pointer, we allow deallocation at any program point and concurrent sharing of these objects amongst all the threads in the program. Experimental results from our .NET CoreCLR implementation on real-world applications show substantial performance gains especially in multithreaded scenarios: up to 3x savings in peak working sets and 2x improvements in runtime.

Hardware support for isolated execution (such as Intel SGX) enables development of applications that keep their code and data confidential even while running in a hostile or compromised host. However, automatically verifying that such applications satisfy confidentiality remains challenging. We present a methodology for designing such applications in a way that enables certifying their confidentiality. Our methodology consists of forcing the application to communicate with the external world through a narrow interface, compiling it with runtime checks that aid verification, and linking it with a small runtime that implements the narrow interface. The runtime includes services such as secure communication channels and memory management. We formalize this restriction on the application as Information Release Confinement (IRC), and we show that it allows us to decompose the task of proving confidentiality into (a) one-time, human-assisted functional verification of the runtime to ensure that it does not leak secrets, (b) automatic verification of the application's machine code to ensure that it satisfies IRC and does not directly read or corrupt the runtime's internal state. We present /CONFIDENTIAL: a verifier for IRC that is modular, automatic, and keeps our compiler out of the trusted computing base. Our evaluation suggests that the methodology scales to real-world applications.

The use of public cloud infrastructure for storing and processing large datasets raises new security concerns. Current solutions propose encrypting all data, and accessing it in plaintext only within secure hardware. Nonetheless, the distributed processing of large amounts of data still involves intensive encrypted communications between different processing and network storage units, and those communications patterns may leak sensitive information. We consider secure implementation of MapReduce jobs, and analyze their intermediate traffic between mappers and reducers. Using datasets that include personal and geographical data, we show how an adversary that observes the runs of typical jobs can infer precise information about their input. We give a new definition of data privacy for MapReduce, and describe two provably-secure, practical solutions. We implement our solutions on top of VC3, a secure implementation of Hadoop, and evaluate their performance.

We present VC3, the first system that allows users to run distributed MapReduce computations in the cloud while keeping their code and data secret, and ensuring the correctness and completeness of their results. VC3 runs on unmodified Hadoop, but crucially keeps Hadoop, the operating system and the hyper visor out of the TCB, thus, confidentiality and integrity are preserved even if these large components are compromised. VC3 relies on SGX processors to isolate memory regions on individual computers, and to deploy new protocols that secure distributed MapReduce computations. VC3 optionally enforces region self-integrity invariants for all MapReduce code running within isolated regions, to prevent attacks due to unsafe memory reads and writes. Experimental results on common benchmarks show that VC3 performs well compared with unprotected Hadoop: VC3's average runtime overhead is negligible for its base security guarantees, 4.5% with write integrity and 8% with read/write integrity.

Application crashes and errors that occur while loading a document are one of the most visible defects of consumer software. While documents become corrupted in various ways---from storage media failures to incompatibility across applications to malicious modifications---the underlying reason they fail to load in a certain application is that their contents cause the application logic to exercise an uncommon execution path which the software was not designed to handle, or which was not properly tested.

Concurrent programming errors arise when threads share data incorrectly. Programmers often avoid these errors by using synchronization to enforce a simple ownership policy: data is either owned exclusively by a thread that can read or write the data, or it is read owned by a set of threads that can read but not write the data. Unfortunately, incorrect synchronization often fails to enforce these policies and memory errors in languages like C and C++ can violate these policies even when synchronization is correct.

Bugs in kernel extensions remain one of the main causes of poor operating system reliability despite proposed techniques that isolate extensions in separate protection domains to contain faults. We believe that previous fault isolation techniques are not widely used because they cannot isolate existing kernel extensions with low overhead on standard hardware. This is a hard problem because these extensions communicate with the kernel using a complex interface and they communicate frequently. We present BGI (Byte-Granularity Isolation), a new software fault isolation technique that addresses this problem. BGI uses efficient byte-granularity memory protection to isolate kernel extensions in separate protection domains that share the same address space. BGI ensures type safety for kernel objects and it can detect common types of errors inside domains. Our results show that BGI is practical: it can isolate Windows drivers without requiring changes to the source code and it introduces a CPU overhead between 0 and 16%. BGI can also find bugs during driver testing. We found 28 new bugs in widely used Windows drivers.

Software vendors collect bug reports from customers to improve the quality of their software. These reports should include the inputs that make the software fail, to enable vendors to reproduce the bug. However, vendors rarely include these inputs in reports because they may contain private user data. We describe a solution to this problem that provides software vendors with new input values that satisfy the conditions required to make the software follow the same execution path until it fails, but are otherwise unrelated with the original inputs. These new inputs allow vendors to reproduce the bug while revealing less private information than existing approaches. Additionally, we provide a mechanism to measure the amount of information revealed in an error report. This mechanism allows users to perform informed decisions on whether or not to submit reports. We implemented a prototype of our solution and evaluated it with real errors in real programs. The results show that we can produce error reports that allow software vendors to reproduce bugs while revealing almost no private information.

Attacks often exploit memory errors to gain control over the execution of vulnerable programs. These attacks remain a serious problem despite previous research on techniques to prevent them. We present write integrity testing (WIT), a new technique that provides practical protection from these attacks. WIT uses points-to analysis at compile time to compute the control-flow graph and the set of objects that can be written by each instruction in the program. Then it generates code instrumented to prevent instructions from modifying objects that are not in the set computed by the static analysis, and to ensure that indirect control transfers are allowed by the control-flow graph. To improve coverage where the analysis is not precise enough, WIT inserts small guards between the original program objects. We describe an efficient implementation with optimizations to reduce space and time overhead. This implementation can be used in practice because it compiles C and C++ programs without modifications, it has high coverage with no false positives, and it has low overhead. WIT's average runtime overhead is only 7% across a set of CPU intensive benchmarks and it is negligible when IO is the bottleneck.

Worm containment must be automatic because worms can spread too fast for humans to respond. Recent work proposed network-level techniques to automate worm containment; these techniques have limitations because there is no information about the vulnerabilities exploited by worms at the network level. We propose Vigilante, a new end-to-end architecture to contain worms automatically that addresses these limitations. In Vigilante, hosts detect worms by instrumenting vulnerable programs to analyze infection attempts. We introduce dynamic data-flow analysis : a broad-coverage host-based algorithm that can detect unknown worms by tracking the flow of data from network messages and disallowing unsafe uses of this data. We also show how to integrate other host-based detection mechanisms into the Vigilante architecture. Upon detection, hosts generate self-certifying alerts (SCAs), a new type of security alert that can be inexpensively verified by any vulnerable host. Using SCAs, hosts can cooperate to contain an outbreak, without having to trust each other. Vigilante broadcasts SCAs over an overlay network that propagates alerts rapidly and resiliently. Hosts receiving an SCA protect themselves by generating filters with vulnerability condition slicing : an algorithm that performs dynamic analysis of the vulnerable program to identify control-flow conditions that lead to successful attacks. These filters block the worm attack and all its polymorphic mutations that follow the execution path identified by the SCA. Our results show that Vigilante can contain fast-spreading worms that exploit unknown vulnerabilities, and that Vigilante's filters introduce a negligible performance overhead. Vigilante does not require any changes to hardware, compilers, operating systems, or the source code of vulnerable programs; therefore, it can be used to protect current software binaries.

Attackers exploit software vulnerabilities to control or crash programs. Bouncer uses existing software instrumentation techniques to detect attacks and it generates filters automatically to block exploits of the target vulnerabilities. The filters are deployed automatically by instrumenting system calls to drop exploit messages. These filters introduce low overhead and they allow programs to keep running correctly under attack. Previous work computes filters using symbolic execution along the path taken by a sample exploit, but attackers can bypass these filters by generating exploits that follow a different execution path. Bouncer introduces three techniques to generalize filters so that they are harder to bypass: a new form of program slicing that uses a combination of static and dynamic analysis to remove unnecessary conditions from the filter; symbolic summaries for common library functions that characterize their behavior succinctly as a set of conditions on the input; and generation of alternative exploits guided by symbolic execution. Bouncer filters have low overhead, they do not have false positives by design, and our results show that Bouncer can generate filters that block all exploits of some real-world vulnerabilities.

Worm containment must be automatic because worms can spread too fast for humans to respond. Recent work has proposed network-level techniques to automate worm containment; these techniques have limitations because there is no information about the vulnerabilities exploited by worms at the network level. We propose Vigilante, a new end-to-end approach to contain worms automatically that addresses these limitations. Vigilante relies on collaborative worm detection at end hosts, but does not require hosts to trust each other. Hosts run instrumented software to detect worms and broadcast self-certifying alerts (SCAs) upon worm detection. SCAs are proofs of vulnerability that can be inexpensively verified by any vulnerable host. When hosts receive an SCA, they generate filters that block infection by analysing the SCA-guided execution of the vulnerable software. We show that Vigilante can automatically contain fast-spreading worms that exploit unknown vulnerabilities without blocking innocuous traffic.

Structured peer-to-peer (P2P) overlay networks provide a useful substrate for building distributed applications. They map object keys to overlay nodes and offer a primitive to send a message to the node responsible for a key. They can implement, for example, distributed hash tables and multicast trees. However, there are concerns about the performance and dependability of these overlays in realistic environments. Several studies have shown that current P2P environments have high churn rates: nodes join and leave the overlay continuously. This paper presents techniques that continuously detect faults and repair the overlay to achieve high dependability and good performance in realistic environments. The techniques are evaluated using large-scale network simulation experiments with fault injection guided by real traces of node arrivals and departures. The results show that previous concerns are unfounded; our techniques can achieve dependable routing in realistic environments with an average delay stretch below two and a maintenance overhead of less than half a message per second per node.

We introduce PIC, a practical coordinate-based mechanism to estimate Internet network distance (i.e., round-trip delay or network hops). Network distance estimation is important in many applications; for example, network-aware overlay construction and server selection. There are several proposals for distance estimation in the Internet but they all suffer from problems that limit their benefit. Most rely on a small set of infrastructure nodes that are a single point of failure and limit scalability. Others use sets of peers to compute coordinates but these coordinates can be arbitrarily wrong if one of these peers is malicious. While it may be reasonable to secure a small set of infrastructure nodes, it is unreasonable to secure all peers. PIC addresses these problems: it does not rely on infrastructure nodes and it can compute accurate coordinates even when some peers are malicious. We present PIC's design, experimental evaluation, and an application to network-aware overlay construction and maintenance.

No abstract available.