The RFID PIA – developed by industry, agreed by regulators

This chapter discusses the privacy impact assessment (PIA) framework endorsed by the European Commission on February 11th, 2011. This PIA, the first to receive the Commission's endorsement, was developed to deal with privacy challenges associated with the deployment of radio frequency identification (RFID) technology, a key building block of the Internet of Things. The goal of this chapter is to present the methodology and key constructs of the RFID PIA Framework in more detail than was possible in the official text. RFID operators can use this article as a support document when they conduct PIAs and need to interpret the PIA Framework. The chapter begins with a history of why and how the PIA Framework for RFID came about. It then proceeds with a description of the endorsed PIA process for RFID applications and explains in detail how this process is supposed to function. It provides examples discussed during the development of the PIA Framework. These examples reflect the rationale behind and evolution of the text's methods and definitions. The chapter also provides insight into the stakeholder debates and compromises that have important implications for PIAs in general.Series: Working Papers on Information Systems, Information Business and Operation

Critical Management Issues for Implementing RFID in Supply Chain Management

The benefits of radio frequency identification (RFID) technology in the supply chain are fairly compelling. It has the potential to revolutionise the efficiency, accuracy and security of the supply chain with significant impact on overall profitability. A number of companies are actively involved in testing and adopting this technology. It is estimated that the market for RFID products and services will increase significantly in the next few years. Despite this trend, there are major impediments to RFID adoption in supply chain. While RFID systems have been around for several decades, the technology for supply chain management is still emerging. We describe many of the challenges, setbacks and barriers facing RFID implementations in supply chains, discuss the critical issues for management and offer some suggestions. In the process, we take an in-depth look at cost, technology, standards, privacy and security and business process reengineering related issues surrounding RFID technology in supply chains

RFID Security and Privacy

The European Commission has published in May 2009 a recommendation "on the implementation of privacy and data protection principles in applications supported by radio-frequency identification", which is designed to provide "guidance to Member States on the design and operation of RFID applications in a lawful, ethical and socially and politically acceptable way, respecting the right to privacy and ensuring protection of personal data." This recommendation requires RFID operators to conduct a "Privacy and Data Protection Impact Assessment" before an RFID application is deployed, and make its results available to the competent authority. The RFID recommendation is also designed to promote "information and transparency on RFID use", in particular through the development of "a common European sign developed by European Standardisation Organisations, with the support of concerned stakeholders", designed "to inform individuals of the presence of readers". The RFID PIA (Privacy and Impact Asssessment) process aims to reach several objectives: * to favour "privacy by design" by helping data controllers to address privacy and data protection before a product or service is deployed, * to help data controllers to address privacy and data protection risks in a comprehensive manner. an opportunity to reduce legal uncertainty and avoid loss of trust from consumers, * to help data controllers and data protection authorities to gain more insight into the privacy and data protection aspects of RFID applications. The industry has proposed a RFID PIA framework which classifies a RFID application into 4 possible levels: Level 0 applications, which essentially cover RFID applications that do not process personal data and where tags are only manipulated by users, and which are rightly excluded from conducting a PIA. Level 1 applications cover applications where no personal data is processed, yet tags are carried by individuals. Level 2 applications process personal data but where tags themselves do not contain personal data. Level 3 applications where tags contain personal data. If the RFID application level is determined to be 1 or above, the RFID operator is then required to conduct a four part analysis of the application, with a level of detail that is proportionate to identified privacy and data protection implications. The first part is used to describe the RFID application. The second part allows highlighting control and security measures. The third part addresses user information and rights. The final part of the proposed PIA framework requires the RFID operator to conclude whether or not the RFID application is ready for deployment. As a result of the PIA process, the RFID operator will produce a PIA report that will be made available to the competent authority. For the industry, only levels 2 and 3 are to be submitted to a PIA because it considers that information contained in a tag at level 1 are not personal. However level 1 arises concerns of Article 29 Working Party because tagged items carried by a person contain unique identifiers that could be read remotely. In turn, these unique identifiers could be used to recognize that particular person through time. It raises the possibility that a person will be tracked without his knowledge by a third party. When a unique identifier is associated to a person, it falls in the definition of personal data set forth in Directive 95/46/EC, regardless of the fact that the "social identity" (name, address, etc.) of the person remains unknown (i.e. he is "identifiable" but not necessarily "identified"). Additionally, the unique number contained in a tag can also serve as a means to remotely identify the nature of items carried by a person, which in turn may reveal information about social status, health, or more. Thus, even in those cases where a tag contains solely a number that is unique within a particular context, and no additional personal data, care must be taken to address potential privacy and security issues if this tag is going to be carried by persons. The Working Party has urged the industry to fully address this issue, by clearly mentioning it in the framework as part of a revised risk assessment approach for level 1. This chapter will address the issue of protecting privacy of RFID tag carriers in a privacy by design model which puts them in a position to decide if they accept or not to be tracked at level 1. In case of a negative decision, tags have to be deactivated. Security measures have also to be taken to protect personal information on RFID tags against information leak which could lead to identity theft

A gap analysis of Internet-of-Things platforms

We are experiencing an abundance of Internet-of-Things (IoT) middleware solutions that provide connectivity for sensors and actuators to the Internet. To gain a widespread adoption, these middleware solutions, referred to as platforms, have to meet the expectations of different players in the IoT ecosystem, including device providers, application developers, and end-users, among others. In this article, we evaluate a representative sample of these platforms, both proprietary and open-source, on the basis of their ability to meet the expectations of different IoT users. The evaluation is thus more focused on how ready and usable these platforms are for IoT ecosystem players, rather than on the peculiarities of the underlying technological layers. The evaluation is carried out as a gap analysis of the current IoT landscape with respect to (i) the support for heterogeneous sensing and actuating technologies, (ii) the data ownership and its implications for security and privacy, (iii) data processing and data sharing capabilities, (iv) the support offered to application developers, (v) the completeness of an IoT ecosystem, and (vi) the availability of dedicated IoT marketplaces. The gap analysis aims to highlight the deficiencies of today's solutions to improve their integration to tomorrow's ecosystems. In order to strengthen the finding of our analysis, we conducted a survey among the partners of the Finnish IoT program, counting over 350 experts, to evaluate the most critical issues for the development of future IoT platforms. Based on the results of our analysis and our survey, we conclude this article with a list of recommendations for extending these IoT platforms in order to fill in the gaps.Comment: 15 pages, 4 figures, 3 tables, Accepted for publication in Computer Communications, special issue on the Internet of Things: Research challenges and solution

A systematic methodology for privacy impact assessments: a design science approach

For companies that develop and operate IT applications that process the personal data of customers and employees, a major problem is protecting these data and preventing privacy breaches. Failure to adequately address this problem can result in considerable damage to the company's reputation and finances, as well as negative effects for customers or employees (data subjects). To address this problem, we propose a methodology that systematically considers privacy issues by using a step-by-step privacy impact assessment (PIA). Existing PIA approaches cannot be applied easily because they are improperly structured or imprecise and lengthy. We argue that companies that employ our PIA can achieve "privacy-by-design", which is widely heralded by data protection authorities. In fact, the German Federal Office for Information Security (BSI) ratified the approach we present in this article for the technical field of RFID and published it as a guideline in November 2011. The contribution of the artefacts we created is twofold: First, we provide a formal problem representation structure for the analysis of privacy requirements. Second, we reduce the complexity of the privacy regulation landscape for practitioners who need to make privacy management decisions for their IT applications

Semantic discovery and reuse of business process patterns

Patterns currently play an important role in modern information systems (IS) development and their use has mainly been restricted to the design and implementation phases of the development lifecycle. Given the increasing significance of business modelling in IS development, patterns have the potential of providing a viable solution for promoting reusability of recurrent generalized models in the very early stages of development. As a statement of research-in-progress this paper focuses on business process patterns and proposes an initial methodological framework for the discovery and reuse of business process patterns within the IS development lifecycle. The framework borrows ideas from the domain engineering literature and proposes the use of semantics to drive both the discovery of patterns as well as their reuse

Social and Political Dimensions of Identity

We study the interior regularity of solutions to the Dirichlet problem Lu = g in Omega, u = 0 in R-nOmega, for anisotropic operators of fractional type Lu(x) = integral(+infinity)(0) dp integral(Sn-1) da(w) 2u(x) - u(x + rho w) - u(x - rho w)/rho(1+2s). Here, a is any measure on Sn-1 (a prototype example for L is given by the sum of one-dimensional fractional Laplacians in fixed, given directions). When a is an element of C-infinity(Sn-1) and g is c(infinity)(Omega), solutions are known to be C-infinity inside Omega (but not up to the boundary). However, when a is a general measure, or even when a is L-infinity(s(n-1)), solutions are only known to be C-3s inside Omega. We prove here that, for general measures a, solutions are C1+3s-epsilon inside Omega for all epsilon > 0 whenever Omega is convex. When a is an element of L-infinity(Sn-1), we show that the same holds in all C-1,C-1 domains. In particular, solutions always possess a classical first derivative. The assumptions on the domain are sharp, since if the domain is not convex and the measure a is singular, we construct an explicit counterexample for which u is not C3s+epsilon for any epsilon > 0 - even if g and Omega are C-infinity