You are provided with the abstract of a scientific paper and the abstracts of the papers it references. Your task is to write a Related Work section for the main paper. This section should concisely summarize and integrate the key points from the cited abstracts, reflecting how they relate to the main paper's contributions and context. In your summary, use numerical citation markers like [1], [2], etc., to refer to the corresponding papers.

Main Abstract:
Identifying the root cause and impact of a system intrusion remains a foundational challenge in computer security. Digital provenance provides a detailed history of the flow of information within a computing system, connecting suspicious events to their root causes. Although existing provenance-based auditing techniques provide value in forensic analysis, they assume that such analysis takes place only retrospectively. Such post-hoc analysis is insufficient for realtime security applications, moreover, even for forensic tasks, prior provenance collection systems exhibited poor performance and scalability, jeopardizing the timeliness of query responses. We present CamQuery, which provides inline, realtime provenance analysis, making it suitable for implementing security applications. CamQuery is a Linux Security Module that offers support for both userspace and in-kernel execution of analysis applications. We demonstrate the applicability of CamQuery to a variety of runtime security applications including data loss prevention, intrusion detection, and regulatory compliance. In evaluation, we demonstrate that CamQuery reduces the latency of realtime query mechanisms, while imposing minimal overheads on system execution. CamQuery thus enables the further deployment of provenance-based technologies to address central challenges in computer security.

Cited Abstract(s):
[1]: Decentralized Information Flow Control (DIFC) is an approach to security that allows application writers to control how data flows between the pieces of an application and the outside world. As applied to privacy, DIFC allows untrusted software to compute with private data while trusted security code controls the release of that data. As applied to integrity, DIFC allows trusted code to protect untrusted software from unexpected malicious inputs. In either case, only bugs in the trusted code, which tends to be small and isolated, can lead to security violations. We present Flume, a new DIFC model that applies at the granularity of operating system processes and standard OS abstractions (e.g., pipes and file descriptors). Flume was designed for simplicity of mechanism, to ease DIFC's use in existing applications, and to allow safe interaction between conventional and DIFC-aware processes. Flume runs as a user-level reference monitor onLinux. A process confined by Flume cannot perform most system calls directly; instead, an interposition layer replaces system calls with IPCto the reference monitor, which enforces data flowpolicies and performs safe operations on the process's behalf. We ported a complex web application (MoinMoin Wiki) to Flume, changingonly 2 of the original code. Performance measurements show a 43 slowdown on read workloadsand a 34 slowdown on write workloads, which aremostly due to Flume's user-level implementation.

[2]: Smartphone users often use private and enterprise data with untrusted third party applications. The fundamental lack of secrecy guarantees in smartphone OSes, such as Android, exposes this data to the risk of unauthorized exfiltration. A natural solution is the integration of secrecy guarantees into the OS. In this paper, we describe the challenges for decentralized information flow control (DIFC) enforcement on Android. We propose contextsensitive DIFC enforcement via lazy polyinstantiation and practical and secure network export through domain declassification. Our DIFC system, Weir, is backwards compatible by design, and incurs less than 4 ms overhead for component startup. With Weir, we demonstrate practical and secure DIFC enforcement on Android.

[3]: 

[4]: HiStar is a new operating system designed to minimize the amount of code that must be trusted. HiStar provides strict information flow control, which allows users to specify precise data security policies without unduly limiting the structure of applications. HiStar's security features make it possible to implement a Unix-like environment with acceptable performance almost entirely in an untrusted user-level library. The system has no notion of superuser and no fully trusted code other than the kernel. HiStar's features permit several novel applications, including privacy-preserving, untrusted virus scanners and a dynamic Web server with only a few thousand lines of trusted code.
Information Flow Control Systems. Previous work on information flow control enforcement at the OS level, such as HiStar [4] , Flume [1] , and Weir [2] , uses labels to define security and integrity contexts that constrain information flows between kernel objects. Labels map to kernel objects, and a process requires decentralised management capabilities to modify its labels. Point-to-point access control decisions are made to evaluate the validity of an information flow. Through transitivity, it is possible to express constraints on a workflow ( collected user information can only be shared with third parties as an aggregate). SELinux [3] provides a similar information flow control mechanism but without decentralised management. A typical way of representing and thinking about information flow in a system is through a directed graph. However, current object labelling abstractions do not take advantage of this representation, and it is difficult to reason about when defining policies. differs from these systems in that it allows the implementation of such mechanisms directly on the graph abstraction.