经济学视野下的隐私问题简析

随着数字经济时代的到来,隐私保护问题越来越受到社会的关注.经济学是探讨隐私和个人数据问题的有力工具.从20世纪60年代开始,经济学家就对隐私问题进行了很多的探讨.围绕着隐私的概念、隐私保护的性质、针对隐私保护的政策等问题开展了激烈争论.从20世纪90年代开始,经济学对于隐私的讨论逐渐转移到数字经济领域,聚焦分析数字经济...     

A multi-layered policy generation and management engine for semantic policy mapping in clouds

The long awaited cloud computing concept is a reality now due to the transformation of computer generations. However, security challenges have become the biggest obstacles for the advancement of this emerging technology. A well-established policy framework is defined in this paper to generate security policies which are compliant to requirements and capabilities. Moreover, a federated policy management schema is introduced based on the policy definition framework and a multi-level policy application to create and manage virtual clusters with identical or common security levels. The proposed model consists in the design of a well-established ontology according to security mechanisms, a procedure which classifies nodes with common policies into virtual clusters, a policy engine to enhance the process of mapping requests to a specific node as well as an associated cluster and matchmaker engine to eliminate inessential mapping processes. The suggested model has been evaluated according to performance and security parameters to prove the efficiency and reliability of this multi-layered engine in cloud computing environments during policy definition, application and mapping procedures.     

Performance Analysis of Privacy Protection System During Data Transfer in MANETs

The openness of a Mobile Adhoc network (MANET) makes it vulnerable to various attacks that can breach privacy, and this demands a privacy protection system. In this paper, we propose a privacy protection system with flexible and adaptable policies to protect privacy during data transfer based on application and context attributes. We also provide the performance analysis model to test the suitability of policies for maintaining privacy, which is essential for the real-time implementation of this system in a resource-limited MANET. Finally, the proposed privacy protection system is compared with previous works using simulations, and the results obtained show the effectiveness of the proposed privacy protection system.     

Personalized Privacy-Preserving Trajectory Data Publishing

Due to the popularity of mobile internet and location-aware devices, there is an explosion of loca-tion and tra jectory data of m oving ob jects. A few pro-posals have been proposed for privacy preserving trajec-tory data publishing, and most of them assume the at-tacks with the same adversarial background knowledge. In practice, different users have different privacy require-ments. Such non-personalized privacy assumption does not meet the personalized privacy requirements, meanwhile, it looses the chance to achieve better utility by taking advan-tage of differences of users' privacy requirements. We study the personalized trajectory k-anonymity criterion for tra-jectory data publication. Specifically, we explore and pro-pose an overall framework which provides privacy preserv-ing services based on users' personal privacy requests, in-cluding tra jectory clustering, editing and publication. We demonstrate the efficiency and effectiveness of our scheme through experiments on real world dataset.     

Developments and Applications of Data Deidentification Technology under Big Data

In this age characterized by rapid growth in the volume of data, data deidentification technologies have become crucial in facilitating the analysis of sensitive information. For instance, healthcare information must be processed through deidentification procedures before being passed to data analysis agencies in order to prevent any exposure of personal details that would violate privacy. As such, privacy protection issues associated with the release of data and data mining have become a popular field of study in the domain of big data. As a strict and verifiable definition of privacy, differential privacy has attracted noteworthy attention and widespread research in recent years. In this study, we analyze the advantages of differential privacy protection mechanisms in comparison to traditional deidentification data protection methods. Furthermore, we examine and analyze the basic theories of differential privacy and relevant studies regarding data release and data mining.     

Towards Privacy Protection in Smart Grid

The smart grid is an electronically controlled electrical grid that connects power generation, transmission, distribution, and consumers using information communication technologies. One of the key characteristics of the smart grid is its support for bi-directional information flow between the consumer of electricity and the utility provider. This two-way interaction allows electricity to be generated in real-time based on consumers’ demands and power requests. As a result, consumer privacy becomes an important concern when collecting energy usage data with the deployment and adoption of smart grid technologies. To protect such sensitive information it is imperative that privacy protection mechanisms be used to protect the privacy of smart grid users. We present an analysis of recently proposed smart grid privacy solutions and identify their strengths and weaknesses in terms of their implementation complexity, efficiency, robustness, and simplicity.     

A framework for discovering relevant patterns using aggregation and intelligent data mining agents in telematics systems

The emerging technology in vehicle telematics drives several stakeholders in this field to consider services that could be beneficial for both clients and the telematics service providers. In particular this paper proposes a novel framework for insurance telematics in Korea using a mobile aggregation agent (AA) and intelligent data mining agent (IDMA). To our knowledge, this model is recent of its kind in this country and the base-line information from driver’s characteristics serves as reference for the flexible insurance policies. We are able to present a use-case scenario and illustrative examples to demonstrate our model. With this flexible insurance framework, customers can manage their own insurance premiums and lower the cost of motoring. Promising applications of this system to business and industries have been recognized and discussed.     

云计算数据安全研究

随着云计算的快速发展和推广应用,在云计算环境中数据安全和隐私保护成为云计算研究中的关键问题。以数据全生命周期模型为基础,提出云计算环境中的数据安全和隐私保护框架,对云计算环境中数据安全和隐私保护的若干关键研究问题,包括密文检索、完整性验证和持有性证明、隐私保护及查询隐私进行了阐述,详细综述了全同态加密的发展、原理、意义及其在云计算数据安全和隐私保护中的应用,并指明了未来的研究方向。     

云存储中加密数据的相似检索技术应用研究

随着云存储的广泛应用，大量数据存储在云服务器。尽管云服务提供很多便利，但数据的隐私及安全性一直是重点关注的问题，为解决数据安全问题需要将外储数据以加密的形式进行存储。加密存储的方式保护了数据不被恶意访问，然而数据的一些重要的基本应用如检索等不能实现。为了在不泄露隐私的条件下实现对加密数据的检索，很多可检索的加密方案被提出。然而，这些方案多数只能处理确切的关键字匹配检索而不能进行相似的关键字检索，相似检索在现实应用中又极其重要。本文提出一个高效的支持加密数据相似检索的方案，为了实现相似密文的检索我们利用一种被称为位置敏感的哈希算法。为了确保数据的机密性和安全性，我们给出了严格的安全定义，并且在安全定义下证明了方案的安全性。      

基于大数据的网络安全的重要性及防范策略

随着大数据时代网络安全问题的日益凸显，保护个人隐私和数据安全变得尤为重要。大数据的广泛应用，使个人数据和敏感信息容易受到黑客或病毒的恶意攻击，为应对这些挑战，加强网络安全意识和防范教育至关重要。文中深入探讨了基于大数据的网络安全的重要性，浅析了防范策略的创新与发展趋势，通过分析网络安全问题的现状与未来发展，以更好地准备和应对新型网络威胁，确保网络在大数据时代下的可信性，实现信息社会的可持续发展。     

Preserving Data Privacy During Data Transfer in MANETs

Mobile adhoc networks consists of large number of mobile nodes, and is usually deployed to transfer data from a sender to a receiver using multi-hop routing. The data being transmitted may contain sensitive information, and undesired disclosure of information can lead to launching of various attacks, thus breaching the data privacy. Earlier works achieve data privacy by using approaches such as data transformation and data perturbation. However, these approaches introduce higher overheads and delays. We propose a computational intelligence based data privacy preserving scheme, where rough set theory is used to anonymize the data during data transfer. Data packets are enclosed within capsules that can be opened only by the designated node, thus preventing the undesired leakage of the data. Also, route between a sender and a receiver is changed dynamically by selecting more than one trusted 1-hop neighbor nodes in each routing step. The proposed data privacy preserving scheme is tested by considering different case studies in a MANET deployed for stock market. Theoretical analysis for data privacy is presented in terms of Information Gain by an attacker and Attacker Overhead, and the performance of proposed scheme against some of the attacks is also discussed. The simulation results show the effectiveness of proposed scheme.

     

Compliance to personal data protection principles: A study of how organizations frame privacy policy notices

This study examines how organizations in Malaysia frame their privacy policy notice to comply with the Personal Data Protection Act (PDPA, 2010) and if these organizations differ in their level of compliance and the readability of their privacy notices. We collected the online privacy polices of 306 organizations from 12 sectors to assess their readability and compliance with PDPA requirements. The results show that private-owned organizations have higher compliance level compared to public-owned organizations. Sectors that hold more personal sensitive data obtain higher compliance scores. Non-governmental organizations demonstrate higher compliance level compared to government-owned organizations. Despite differences in the compliance scores, most organizations fail to meet the requirements of the PDPA. Our study also reveals that readability has a negative correlation with the compliance score because simple and shorter version of the privacy policies often lack detailed information. Our findings provide valuable insights into organizations’ privacy policy compliance across different sectors in Malaysia. Specifically, the Malaysian authority should implement more effective mechanisms to enforce the compliance of the PDPA. Organizations should also take corrective actions to improve the compliance scores of their online privacy policies.     

Examining Internet privacy policies within the context of user privacy values

Internet privacy policies describe an organization's practices on data collection, use, and disclosure. These privacy policies both protect the organization and signal integrity commitment to site visitors. Consumers use the stated website policies to guide browsing and transaction decisions. This paper compares the classes of privacy protection goals (which express desired protection of consumer privacy rights) and vulnerabilities (which potentially threaten consumer privacy) with consumer privacy values. For this study, we looked at privacy policies from nearly 50 websites and surveyed over 1000 Internet users. We examined Internet users' major expectations about website privacy and revealed a notable discrepancy between what privacy policies are currently stating and what users deem most significant. Our findings suggest several implications to privacy managers and software project managers. Results from this study can help managers determine the kinds of policies needed to both satisfy user values and ensure privacy-aware website development efforts.     

大数据安全研究综述

随着人工智能、云计算、移动互联网和物联网等技术的融合发展,传统的基于边界安全域和基于已知特征库的网络安全防护方式已经无法有效应对大数据环境下新的安全威胁。通过对大数据环境下面临的安全问题和挑战进行分析,提出基于大数据分析和威胁情报共享为基础的大数据协同安全防护体系,将大数据安全技术框架、数据安全治理、安全测评和运维管理相结合,在数据分类分级和全生命周期安全的基础上,体系性的解决大数据不同层次的安全问题。基于该安全防护体系,分析了数据安全的关键技术及其目前的发展现状,并展望和分析了大数据安全领域面临的挑战。全面的分析和研究了大数据安全的威胁、政策、标准、方案、关键技术和挑战,对开展大数据安全建设和工程应用有重要参考意义。     

Performance Analysis of Fair Channel Sharing Policies in an Integrated Cellular Voice/Data Network

We study channel sharing in an integrated cellular voice/data network with a finite queue for data call requests that cannot be served immediately upon arrival. Using analytical techniques, a comparison of different fair channel sharing policies is made. As a main result, a closed-form expression is derived for the expected sojourn time (waiting time plus transfer time) of a data call, conditional on its size, indicating that the sojourn time is proportional to the call size. This attractive proportionality result establishes an additional fairness property for the channel sharing policies proposed in the paper. Additionally, as a valuable intermediate result, the conditional expected sojourn time of an admitted data call is obtained, given the system state at arrival, which may serve as an appreciated feedback information service to the data source. An extensive numerical study is included to compare the proposed policies and to obtain insight in the performance effects of the various system and policy parameters.     

基于位置语义的路网位置隐私保护

移动用户在享受基于位置的服务（LBS）的同时受到位置隐私泄露的威胁,因而提供有效的位置隐私保护策略至关重要。传统的位置隐私保护方法主要采用空间匿名的方式,若攻击者获得了更多与匿名空间相关的背景知识,尤其是与位置相关的语义信息,就会严重降低匿名效果。为了防止由位置语义分析造成的敏感位置信息泄露,并根据移动用户活动范围大多限定为道路网络的特点,提出一种基于位置语义的路网位置隐私保护方法,充分考虑了用户的个性化隐私需求,并通过实验验证了方法的可行性及有效性。     

面向机器学习的隐私保护关键技术研究综述

随着信息通信技术的发展,机器学习已经成为多个研究领域与垂直行业必不可少的技术工具。然而,机器学习所需数据中往往包含了大量的个人信息,使其隐私保护面临风险与挑战,受到了越来越多的关注。对现有机器学习下隐私保护法规政策与标准化现状进行梳理,对适用于机器学习的隐私保护技术进行详细介绍与分析。隐私保护算法通常会对数据质量、通信开支与模型表现等造成影响,因此对于隐私保护算法的评估应当进行多维度的综合评估。总结了适用于机器学习应用的隐私保护性能评估指标,并指出隐私保护需要考虑对数据质量、通信开支以及模型准确率等之间的影响。     

CPFinder: Finding an unknown caller's profession from anonymized mobile phone data

Identifying an unfamiliar caller's profession is important to protect citizens' personal safety and property. Owing to the limited data protection of various popular online services in some countries, such as taxi hailing and ordering takeouts, many users presently encounter an increasing number of phone calls from strangers. The situation may be aggravated when criminals pretend to be such service delivery staff, threatening the user individuals as well as the society. In addition, numerous people experience excessive digital marketing and fraudulent phone calls because of personal information leakage. However, previous works on malicious call detection only focused on binary classification, which does not work for the identification of multiple professions. We observed that web service requests issued from users' mobile phones might exhibit their application preferences, spatial and temporal patterns, and other profession-related information. This offers researchers and engineers a hint to identify unfamiliar callers. In fact, some previous works already leveraged raw data from mobile phones (which includes sensitive information) for personality studies. However, accessing users' mobile phone raw data may violate the more and more strict private data protection policies and regulations (e.g., General Data Protection Regulation). We observe that appropriate statistical methods can offer an effective means to eliminate private information and preserve personal characteristics, thus enabling the identification of the types of mobile phone callers without privacy concerns. In this paper, we develop CPFinder —- a system that exploits privacy-preserving mobile data to automatically identify callers who are divided into four categories of users: taxi drivers, delivery and takeouts staffs, telemarketers and fraudsters, and normal users (other professions). Our evaluation of an anonymized dataset of 1,282 users over a period of 3 months in Shanghai City shows that the CPFinder can achieve accuracies of more than 75.0% and 92.4% for multiclass and binary classifications, respectively.     

面向多路径传输中数据隐私性分析的安全模型

In order to investigate the enhancement of data privacy by distributing data packets via multiple paths, this paper formulates a security model and analyzes the privacy problem in multipath scenarios leveraging information theoretic concept. Based on proposed model, a privacy function related to the path number is discussed. We heuristically recommend the optimal path number and analyze the tradeoff among the performance, resource consumption and privacy. For reducing the information leakage, the data schedule algorithms are also proposed. The analytical model can provide guidelines for the multipath protocol design.     

PriMa: a comprehensive approach to privacy protection in social network sites

With social networks (SNs) allowing their users to host large amounts of personal data on their platforms, privacy protection mechanisms are becoming increasingly important. The current privacy protection mechanisms offered by SNs mostly enforce access control policies based on users’ privacy settings. The task of setting privacy preferences may be tedious and confusing for the average user, who has hundreds of connections (e.g., acquaintances, colleagues, friends, etc.) and maintains an extensive profile on his main SN. Hence, users often end up with policies that do not sufficiently protect their personal information, thus facilitating potential privacy breaches and information misuse. In this paper, we propose PriMa (Privacy Manager), a privacy protection mechanism that supports semiautomated generation of access rules for users’ profile information, filling the gap between the privacy management needs of SN users and the existing SNs’ privacy protection mechanisms. PriMa access rules are generated using a multicriteria algorithm, so as to account for an extensive set of criteria to be considered when dealing with access control in SN sites. The resulting rules are simple yet powerful specifications, indicating the adequate level of protection for each user, and are dynamically adapted to the ever-changing requirements of the users’ preferences and SN configuration. We have implemented PriMa on a Drupal platform and as a third-party Facebook application. We have evaluated the performance of the PriMa application with respect to access rule generation.