计量经济分析 = Econometric analysis

Randomized Aggregatable Privacy-Preserving Ordinal Response, or RAPPOR, is a technology for crowdsourcing statistics from end-user client software, anonymously, with strong privacy guarantees. In short, RAPPORs allow the forest of client data to be studied, without permitting the possibility of looking at individual trees. By applying randomized response in a novel manner, RAPPOR provides the mechanisms for such collection as well as for efficient, high-utility analysis of the collected data. In particular, RAPPOR permits statistics to be collected on the population of client-side strings with strong privacy guarantees for each client, and without linkability of their reports. This paper describes and motivates RAPPOR, details its differential-privacy and utility guarantees, discusses its practical deployment and properties in the face of different attack models, and, finally, gives results of its application to both synthetic and real-world data.

RAPPOR: Randomized Aggregatable Privacy-Preserving Ordinal Response

In his seminal book, Shewhart (1931) makes no demand on the distribution of the characteristic to be plotted on a control chart. How then can we explain the idea that normality is, if not required, at least highly desirable? I believe that it has come about through the many statistical studies of control-chart behavior. If one is to study how a control chart behaves, it is necessary to relate it to some distribution. The obvious choice is the normal distribution because of its ubiquity as a satisfactory model. This is bolstered by the existence of the Central Limit Theorem.

Sampling: Design and Analysis

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Serverless cloud computing handles virtually all the system administration operations needed to make it easier for programmers to use the cloud. It provides an interface that greatly simplifies cloud programming, and represents an evolution that parallels the transition from assembly language to high-level programming languages. This paper gives a quick history of cloud computing, including an accounting of the predictions of the 2009 Berkeley View of Cloud Computing paper, explains the motivation for serverless computing, describes applications that stretch the current limits of serverless, and then lists obstacles and research opportunities required for serverless computing to fulfill its full potential. Just as the 2009 paper identified challenges for the cloud and predicted they would be addressed and that cloud use would accelerate, we predict these issues are solvable and that serverless computing will grow to dominate the future of cloud computing.

Cloud Programming Simplified: A Berkeley View on Serverless Computing

Serverless computing has emerged as a new cloud computing paradigm, where an application consists of individual functions that can be separately managed and executed. However, existing serverless platforms normally isolate and execute functions in separate containers, and do not exploit the interactions among functions for performance. These practices lead to high startup delays for function executions and inefficient resource usage. This paper presents SAND, a new serverless computing system that provides lower latency, better resource efficiency and more elasticity than existing serverless platforms. To achieve these properties, SAND introduces two key techniques: 1) application-level sandboxing, and 2) a hierarchical message bus. We have implemented and deployed a complete SAND system. Our results show that SAND outperforms the state-of-the-art serverless platforms significantly. For example, in a commonly-used image processing application, SAND achieves a 43% speedup compared to Apache OpenWhisk.

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SAND: Towards High-Performance Serverless Computing.

Major cloud computing operators provide powerful monitoring tools to understand the current (and prior) state of the distributed systems deployed in their infrastructure. While such tools provide a detailed monitoring mechanism at scale, they also pose a significant challenge for the application developers/operators to transform the huge space of monitored metrics into useful insights. These insights are essential to build effective management tools for improving the efficiency, resiliency, and dependability of distributed systems. This paper reports on our experience with building and deploying Sieve---a platform to derive actionable insights from monitored metrics in distributed systems. Sieve builds on two core components: a metrics reduction framework, and a metrics dependency extractor. More specifically, Sieve first reduces the dimensionality of metrics by automatically filtering out unimportant metrics by observing their signal over time. Afterwards, Sieve infers metrics dependencies between distributed components of the system using a predictive-causality model by testing for Granger Causality. We implemented Sieve as a generic platform and deployed it for two microservices-based distributed systems: OpenStack and Share-Latex. Our experience shows that (1) Sieve can reduce the number of metrics by at least an order of magnitude (10 -- 100×), while preserving the statistical equivalence to the total number of monitored metrics; (2) Sieve can dramatically improve existing monitoring infrastructures by reducing the associated overheads over the entire system stack (CPU---80%, storage---90%, and network---50%); (3) Lastly, Sieve can be effective to support a wide-range of workflows in distributed systems---we showcase two such workflows: Orchestration of autoscaling, and Root Cause Analysis (RCA).

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Sieve: actionable insights from monitored metrics in distributed systems

Today, websites commonly use third party web analytics services t obtain aggregate information about users that visit their sites. This information includes demographics and visits to other sites as well as user behavior within their own sites. Unfortunately, to obtain this aggregate information, web analytics services track individual user browsing behavior across the web. This violation of user privacy has been strongly criticized, resulting in tools that block such tracking as well as anti-tracking legislation and standards such as Do-Not-Track. These efforts, while improving user privacy, degrade the quality of web analytics. This paper presents the first design of a system that provides web analytics without tracking. The system gives users differential privacy guarantees, can provide better quality analytics than current services, requires no new organizational players, and is practical to deploy. This paper describes and analyzes the design, gives performance benchmarks, and presents our implementation and deployment across several hundred users.

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Non-tracking web analytics

There is a growing body of research on mechanisms for preserving online user privacy while still allowing aggregate queries over private user data. A common approach is to store user data at users' devices, and to query the data in such a way that a differentially private noisy result is produced without exposing individual user data to any system component. A particular challenge is to design a system that scales well while limiting how much the malicious users can distort the result. This paper presents SplitX, a high-performance analytics system for making differentially private queries over distributed user data. SplitX is typically two to three orders of magnitude more efficient in bandwidth, and from three to five orders of magnitude more efficient in computation than previous comparable systems, while operating under a similar trust model. SplitX accomplishes this performance by replacing public-key operations with exclusive-or operations. This paper presents the design of SplitX, analyzes its security and performance, and describes its implementation and deployment across 416 users.

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SplitX: high-performance private analytics

Incremental processing of large-scale data is an increasingly important problem, given that many processing jobs run repeatedly with similar inputs, and that the de facto standard programmingmodel (MapReduce) was not designed to efficiently process small updates. As a result, new systems specifically targeting this problem (e.g., Google Percolator, or Yahoo! CBP) have been proposed. Unfortunately, these approaches require the adoption of a new programming model, breaking compatibility with existing programs, and increasing the burden on the programmer, who now is required to devise an incremental update mechanism. We claim that automatic incremental processing of large-scale data is possible by leveraging previous results from the algorithms and programming languages communities. As an example, we describe how Map Reduce can be improved to efficiently handle small input changes by automatically incrementalizing existing MapReduce computations, without breaking backward compatibility or demanding programmers to adopt a new programming approach.

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Istemi Ekin Akkus

Papers

SAND: Towards High-Performance Serverless Computing.

Sieve: actionable insights from monitored metrics in distributed systems

Non-tracking web analytics

SplitX: high-performance private analytics

Large-scale incremental data processing with change propagation