Privacy in pervasive environments: next generation labeling protocols

In pervasive environments, privacy is likely to be a major issue for users, and users will want to be notified of potential data capture. To provide notice to users, this paper argues for what it calls labeling protocols, technical mechanisms through which users can be informed of data requests and their consequences. Recent experiences with the Platform for Privacy Preferences Project (P3P), an attempt to provide privacy mechanisms for the Web, suggest important lessons for the design of a next generation labeling protocol that will be usable and useful in pervasive environments. This paper examines the P3P lessons and open issues with an eye to pervasive requirements.     

Understanding and measuring the urban pervasive infrastructure

The increasing popularity of mobile computing devices has allowed for new research and application areas. Specifically, urban areas exhibit an elevated concentration of such devices enabling potential ad-hoc co-operation and sharing of resources among citizens. Here, we argue that people, architecture and technology together provide the infrastructure for these applications and an understanding of this infrastructure is important for effective design and development. We focus on describing the metrics for describing this infrastructure and elaborate on a set of observation, analysis and simulation methods for capturing, deriving and utilising those metrics.

Data quality and query cost in pervasive sensing systems

This research is motivated by large-scale pervasive sensing applications. We examine the benefits and costs of caching data for such applications. We propose and evaluate several approaches to querying for, and then caching data in a sensor field data server. We show that for some application requirements (i.e., when delay drives data quality), policies that emulate cache hits by computing and returning approximate values for sensor data yield a simultaneous quality improvement and cost saving. This win–win is because when system delay is sufficiently important, the benefit to both query cost and data quality achieved by using approximate values outweighs the negative impact on quality due to the approximation. In contrast, when data accuracy drives quality, a linear trade-off between query cost and data quality emerges. We also identify caching and lookup policies for which the sensor field query rate is bounded when servicing an arbitrary workload of user queries. This upper bound is achieved by having multiple user queries share the cost of a single sensor field query. Finally, we demonstrate that our results are robust to the manner in which the environment being monitored changes using models for two different sensing systems.     

Pervasive location acquisition technologies: Opportunities and challenges for geospatial studies

The rapid development and increasing availability of various location acquisition technologies provide geospatial studies with both opportunities and challenges. These opportunities and challenges are discussed in this paper focusing on the following three aspects: the massive acquisition of location data and data quality, the analysis of massive location data and pattern discovery, and privacy protection for massive location data. This paper examines the current status of and the potential opportunities for geospatial research in these three areas and notes the major challenges. Finally, the development of this special issue is described, and the four articles included in this special issue are presented.     

Synthetic data generation: State of the art in health care domain

Recent progress in artificial intelligence and machine learning has led to the growth of research in every aspect of life including the health care domain. However, privacy risks and legislations hinder the availability of patient data to researchers. Synthetic data (SD) has been regarded as a privacy-safe alternative to real data and has lately been employed in many research and academic endeavors. This growing body of research needs to be consolidated for the researchers and practitioners to gain a quick and fruitful comprehension of the state of the art in synthetic data generation in health care. The purpose of this study is to collate and synthesize the current state of synthetic data generation following a narrative review of 70 peer-reviewed studies discussing privacy-preserving synthetic medical data generation techniques. The literature shows the effectiveness of synthetic datasets for different applications in research, academics, and testing according to existing statistical and task-based utility metrics. However, the focus on longitudinal synthetic data seems deficient. Moreover, a unified metric for generic quality assessment of synthetic data is lacking. The results of this review will serve as a quick reference guide for the researchers and practitioners in the healthcare domain to select a suitable synthetic data strategy for their application based on its strengths and weaknesses and pave the path for further research and development in healthcare.     

A novel anonymization algorithm: Privacy protection and knowledge preservation

In data mining and knowledge discovery, there are two conflicting goals: privacy protection and knowledge preservation. On the one hand, we anonymize data to protect privacy; on the other hand, we allow miners to discover useful knowledge from anonymized data. In this paper, we present an anonymization method which provides both privacy protection and knowledge preservation. Unlike most anonymization methods, where data are generalized or permuted, our method anonymizes data by randomly breaking links among attribute values in records. By data randomization, our method maintains statistical relations among data to preserve knowledge, whereas in most anonymization methods, knowledge is lost. Thus the data anonymized by our method maintains useful knowledge for statistical study. Furthermore, we propose an enhanced algorithm for extra privacy protection to tackle the situation where the user’s prior knowledge of original data may cause privacy leakage. The privacy levels and the accuracy of knowledge preservation of our method, along with their relations to the parameters in the method are analyzed. Experiment results demonstrate that our method is effective on both privacy protection and knowledge preservation comparing with existing methods.     

边缘计算: 现状与展望

近年来,信息技术的不断变革伴随数据量的急剧爆发,使主流的云计算解决方案面临实时性差、带宽受限、高能耗、维护费用高、隐私安全等问题. 边缘智能的出现与快速发展有效缓解了此类问题,它将用户需求处理下沉到边缘,避免了海量数据在网络中的流动,得到越来越多的关注. 由于边缘计算中资源性能普遍较低,通过资源实现协同推理正成为热点.通过对边缘智能发展的趋势分析,得出边缘协同推理目前仍处于增长期,还未进入稳定发展期. 因此,在对边缘协同推理进行充分调研的基础上,将边缘协同推理划分为智能化方法与协同推理架构2个部分,分别对其中涉及到的关键技术进行纵向归纳整理,并从动态场景角度出发,对每种关键技术进行深入分析,对不同关键技术进行横向比较以及适用场景分析.最后对动态场景下的边缘协同推理给出值得研究的若干发展方向.

     

基于联盟链的电子健康记录隐私保护和共享

医院正在逐渐采用电子健康记录(EHR)的方式去记录患者的医疗信息。然而,医疗数据的隐私性和EHR标准的差异化阻碍了医疗数据在病人和医院之间的共享。因此,针对隐私信息泄露和难于共享的问题,提出了一个基于联盟链的隐私保护数据共享模型。此外,基于匿名算法提出了(p,α,k)匿名隐私算法,能够解决EHR隐私信息泄露的问题。通过理论分析和实验证明,提出的基于联盟链和(p,α,k)隐私匿名算法模型能够在保护数据隐私的前提下,实现病人和医院之间的数据安全共享。对比前人的模型,该模型具有所需节点少、减少主链压力、容错性强和病人对EHR完全控制等优势。     

基于同态加密的物联网隐私保护计算方案

在节点能量、计算能力有限、存储空间较小、网络链路容易断裂的物联网中,节点需要将收集到的数据包快速、安全地传送给基站,针对节点在进行路由选择时可能导致的隐私泄露问题,提出了一种基于同态加密的新型物联网信息隐私计算方案。在路径不确定的情况下,该方案能保证节点收集的数据信息以最大可能性发送给目标节点,同时在进行节点之间相遇概率的比较时,采用同态加密函数进行加密,不仅隐藏了节点之间的相遇概率,也有利于节点之间相遇概率的比较,保护了相关节点的隐私。实验表明,该计算方案比同类型的方法计算量减少24%,最后,分析了该方案的安全性。     

Composing software services in the pervasive computing environment: Languages or APIs?

The pervasive computing environment will be composed of heterogeneous services. In this work, we have explored how a domain specific language for service composition can be implemented to capture the common design patterns for service composition, yet still retain a comparable performance to other systems written in mainstream languages such as Java. In particular, we have proposed the use of the method delegation design pattern, the resolution of service bindings through the use of dynamically adjustable characteristics and the late binding of services as key features in simplifying the service composition task. These are realised through the Scooby language, and the approach is compared to the use of APIs to define adaptable services.     

State of the art and challenges in mineral processing control

The objective of process control in the mineral industry is to optimise the recovery of the valuable minerals, while maintaining the quality of the concentrates delivered to the metal extraction plants. The paper presents a survey of the control approaches for ore size reduction and mineral separation processes. The present limitations of the measurement instrumentation are discussed, as well as the methods to upgrade the information delivered by the sensors. In practice, the overall economic optimisation goal must be hierarchically decomposed into simpler control problems. Model-based and AI methods are reviewed, mainly for grinding and flotation processes, and classified as mature, active or emerging.     

面向数据库应用的隐私保护研究综述

随着数据挖掘和数据发布等数据库应用的出现与发展,如何保护隐私数据和防止敏感信息泄露成为当前面临的重大挑战.隐私保护技术需要在保护数据隐私的同时不影响数据应用.根据采用技术的不同,出现了数据失真、数据加密、限制发布等隐私保护技术.文中对隐私保护领域已有研究成果进行了总结,对各类隐私保护技术的基本原理、特点进行了阐述,还详细介绍了各类技术的典型应用,并重点介绍了当前该领域的研究热点:基于数据匿名化的隐私保护技术.在对已有技术深入对比分析的基础上,指出了隐私保护技术的未来发展方向.     

射频识别(RFID)系统安全对策技术研究的概述

如同许多技术一样,随着射频识别技术的不断发展,射频识别系统也将遍布于我们日常生活的每个角落。射频识别系统在带给我们巨大的生产效益和可观的商业价值的同时也威胁到个人和组织的安全和隐私。在简要介绍了射频识别系统所面临的安全和隐私威胁的主要形式后;并针对射频识别系统的安全对策进行了以理论分析,特别是较详细地说明如何利用了密码学方面的相关知识来解决射频识别系统的安全问题。     

Hybrid Approach for Privacy Enhancement in Data Mining Using Arbitrariness and Perturbation

Imagine numerous clients, each with personal data; individual inputs are severely corrupt, and a server only concerns the collective, statistically essential facets of this data. In several data mining methods, privacy has become highly critical. As a result, various privacy-preserving data analysis technologies have emerged. Hence, we use the randomization process to reconstruct composite data attributes accurately. Also, we use privacy measures to estimate how much deception is required to guarantee privacy. There are several viable privacy protections; however, determining which one is the best is still a work in progress. This paper discusses the difficulty of measuring privacy while also offering numerous random sampling procedures and statistical and categorized data results. Furthermore, this paper investigates the use of arbitrary nature with perturbations in privacy preservation. According to the research, arbitrary objects (most notably random matrices) have "predicted" frequency patterns. It shows how to recover crucial information from a sample damaged by a random number using an arbitrary lattice spectral selection strategy. This filtration system's conceptual framework posits, and extensive practical findings indicate that sparse data distortions preserve relatively modest privacy protection in various situations. As a result, the research framework is efficient and effective in maintaining data privacy and security.     

大数据时代的个人隐私保护

随着信息技术的发展,以Web2.0技术为基础的博客、微博、社交网络等新兴服务和物联网以前所未有的发展速度产生了类型繁多的数据,而云计算为数据的存储提供了基础平台,这一切造就了大数据时代的正式到来.大数据中蕴藏着巨大的价值,是企业的宝贵财富.但大数据同时也带来了巨大的挑战,个人隐私保护问题就是其中之一.迅速发展的互联网已经成为人们生活中不可或缺的一部分,人们在网络上留下了许多数据足迹,这些数据足迹具有累积性和关联性,将多处数据足迹聚集在一起,就可以发现个人的隐私信息.恶意分子利用这些信息进行欺诈等行为,给个人的生活带来了许多麻烦或经济损失,因此大数据的个人隐私问题引起了工业界和学术界的广泛关注.首先介绍了大数据时代个人隐私保护的相关概念,讨论了个人隐私保护面临的挑战和研究问题;然后从数据层、应用层以及数据展示层叙述了个人隐私保护所使用的技术,探讨了个人隐私保护的相关法律以及行业规范的几个重要方面;最后提出了大数据个人隐私保护的进一步研究方向.     

Contextual music information retrieval and recommendation: State of the art and challenges

Increasing amount of online music content has opened new opportunities for implementing new effective information access services–commonly known as music recommender systems–that support music navigation, discovery, sharing, and formation of user communities. In the recent years a new research area of contextual (or situational) music recommendation and retrieval has emerged. The basic idea is to retrieve and suggest music depending on the user’s actual situation, for instance emotional state, or any other contextual conditions that might influence the user’s perception of music. Despite the high potential of such idea, the development of real-world applications that retrieve or recommend music depending on the user’s context is still in its early stages. This survey illustrates various tools and techniques that can be used for addressing the research challenges posed by context-aware music retrieval and recommendation. This survey covers a broad range of topics, starting from classical music information retrieval (MIR) and recommender system (RS) techniques, and then focusing on context-aware music applications as well as the newer trends of affective and social computing applied to the music domain.     

Security on VANETs: Privacy, misbehaving nodes, false information and secure data aggregation

This article is a position paper on the current security issues in Vehicular Ad hoc Networks (VANETs). VANETs face many interesting research challenges in multiple areas, from privacy and anonymity to the detection and eviction of misbehaving nodes and many others in between. Multiple solutions have been proposed to address those issues. This paper surveys the most relevant while discussing its benefits and drawbacks. The paper explores the newest trends in privacy, anonymity, misbehaving nodes, the dissemination of false information and secure data aggregation, giving a perspective on how we foresee the future of this research area.First, the paper discusses the use of Public Key Infrastructure (PKI) (and certificates revocation), location privacy, anonymity and group signatures for VANETs. Then, it compares several proposals to identify and evict misbehaving and faulty nodes. Finally, the paper explores the differences between syntactic and semantic aggregation techniques, cluster and non-cluster based with fixed and dynamic based areas, while presenting secure as well as probabilistic aggregation schemes.     

Live digital,remember digital: State of the art and research challenges

The so called trend “live digital, remember digital” is acquiring higher relevance within the international research community, due to its several appealing challenges in a multitude of different fields within the Information and Communication Technologies. Today, many people live daily connected to the Internet through their mobile phones, laptops, tablets, etc. and the need to audit or log every single digital interaction emerges in many environments. By seamlessly recording those digital interactions and storing them in a privacy-preserving fashion, a number of benefits are brought to end users, like the provision of user-tailored services, amongst many others. In this paper we will particularly focus on the study of the security and privacy challenges within this field, as well as on the analysis of the currently existing solutions addressing these issues and we will propose an architecture for the so called live digital systems.