1. 在参与社会感知中的自我调节信息共享（Self-regulatory information sharing in participatory social sensing）

FromEPJ Data Science 2016 5:14 April 3, 3:00 PM

By Evangelos Pournaras, Jovan Nikolic, Pablo Velásquez, Marcello Trovati, Nik Bessis and Dirk Helbing

(Translated by -阎赫, Edited by Jake )

在社会感知的应用中，参与性一直受到隐私威胁的挑战。大规模访问公民的数据就使得监控和可能造成社会分离现象的歧视行为得到允许；而它的好处是为更知情的决策提供所需的精确计算与分析，更有效的政策和由物联网技术支持的技术-社会-经济系统的调节。

以前的工作要么着眼于隐私保护，要么着眼于大数据分析，本文与这些工作不同，我们提出了一种自我调节的信息共享系统为这个隔阂搭建桥梁。我们通过将信息共享建模为一种计算市场中的供求系统以达到此目的。公民处于供方，他们在所共享的信息层面做出受激励而又自主的决策。数据汇集处于需求方，它们为了精确的分析，为公民接受所需的数据提供报酬。

这个系统用来自两个应用领域的两个真实数据集来进行实证地评估：（1）智能网格和（2）手机感应。我们的实验结果可以量化出在不同的实验设定下隐私保护、分析的准确性及提供报酬引发的成本之间的权衡。

调查结果显示隐私保护是否高，取决于参与的公民数量和归纳的数据的类型。此外，汇总数据分析能够容忍高度局部错误，对整体准确度没有重大影响。换句话说，局部错误可以被抵消掉。我们可以优化报酬的公平程度以使得能够共享更多重大意义信息的公民会接受更高的报酬。所有这些调查结果激励了一种真正的去中心化的、合乎道德规范的数据分析的范式出现。

原文

Participation in social sensing applications is challenged by privacy threats. Large-scale access to citizens’ data allow surveillance and discriminatory actions that may result in segregation phenomena in society. On the contrary are the benefits of accurate computing analytics required for more informed decision-making, more effective policies and regulation of techno-socio-economic systems supported by ‘Internet-of Things’ technologies. In contrast to earlier work that either focuses on privacy protection or Big Data analytics, this paper proposes a self-regulatory information sharing system that bridges this gap. This is achieved by modeling information sharing as a supply-demand system run by computational markets. On the supply side lie the citizens that make incentivized but self-determined decisions about the level of information they share. On the demand side stand data aggregators that provide rewards to citizens to receive the required data for accurate analytics. The system is empirically evaluated with two real-world datasets from two application domains: (i) Smart Grids and (ii) mobile phone sensing. Experimental results quantify trade-offs between privacy-preservation, accuracy of analytics and costs from the provided rewards under different experimental settings. Findings show a higher privacy-preservation that depends on the number of participating citizens and the type of data summarized. Moreover, analytics with summarization data tolerate high local errors without a significant influence on the global accuracy. In other words, local errors cancel out. Rewards can be optimized to be fair so that citizens with more significant sharing of information receive higher rewards. All these findings motivate a new paradigm of truly decentralized and ethical data analytics.

原文链接：

http://dx.doi.org/10.1140/epjds/s13688-016-0074-4

2. 一种度量系统复杂性的通用框架（A general framework for measuring system complexity）

From onlinelibrary.wiley.com April 3, 12:47 AM

By Mahmoud Efatmaneshnik and Michael J. Ryan

(Translated by – 龚力，edited by 傅渥成)

在这项工作中，我们的动机基于此前的系统复杂性度量方法都没有直接解决最本质的问题——任何特定的物质或事物的复杂性具有一个十分显著的主观成分，其复杂性程度取决于观察者采用的参考系。

因此，任何试图通过移除系统的主观性来度量其复杂性的方法，都不能解决这一非常重要的方面；反之，对于纯粹主观的复杂性度量方法也存在合理的担忧，简单来讲，因为如果参考系本身也是复杂和主观的，那么这种方法必然是不可验证的。我们通过引入主观简单性（subjective simplicity）的概念来解决这个问题——虽然一个合理和可验证的主观复杂性程度值难以直接度量，但是在一个给定的上下文中度量什么是“简单”却是可能的，以此“简单”作为参考，可以将主观复杂性当作到“简单”的距离。然后，我们提出一个广泛的、适用于任何领域的复杂性度量方法，并提供了一些如何将它应用到工程系统中的例子。

原文：

In this work, we are motivated by the observation that previous considerations of appropriate complexity measures have not directly addressed the fundamental issue that the complexity of any particular matter or thing has a significant subjective component in which the degree of complexity depends on available frames of reference. Any attempt to remove subjectivity from a suitable measure therefore fails to address a very significant aspect of complexity. Conversely, there has been justifiable apprehension toward purely subjective complexity measures, simply because they are not verifiable if the frame of reference being applied is in itself both complex and subjective. We address this issue by introducing the concept of subjective simplicity—although a justifiable and verifiable value of subjective complexity may be difficult to assign directly, it is possible to identify in a given context what is “simple” and, from that reference, determine subjective complexity as distance from simple. We then propose a generalized complexity measure that is applicable to any domain, and provide some examples of how the framework can be applied to engineered systems.

原文链接：

http://dx.doi.org/10.1002/cplx.21767