Brief descriptions of the projects follow:

SmartNurse is a FETLaunchpad winning proposal originated in the SmartSociety project that aims at developing (a) smart teaching assistant(s) for individualised training of student nurses. Such systems will offer information on demand (e.g. instant feedback, regulations or quick-check information, hints, …), and also provide feedback if the activities performed by the trainee during training sessions are not performed to required training goals. This application will allow student nurses to learn faster and get specific individual support.

Abstract: Healthcare Information Systems typically fall into the group of systems in which the need of data sharing conflicts with the privacy. A myriad of these systems have to, however, constantly communicate among each other. One of the ways to address the dilemma between data sharing and privacy is to use data obfuscation by lowering data accuracy to guarantee patient’s privacy while retaining its usefulness. Even though many obfuscation methods are able to handle numerical values, the obfuscation of non-numerical values (e.g., textual information) is not as trivial, yet extremely important to preserve data utility along the process. In this paper, we preliminary investigate how to exploit ontologies to create obfuscation mechanism for releasing personal and electronic health records (PHR and EHR) to selected audiences with different degrees of obfuscation. Data minimisation and access control should be supported to enforce different actors, e.g., doctors, nurses and managers, will get access to no more information than needed for their tasks. Besides that, ontology-based obfuscation can also be used for the particular case of data anonymisation. In such case, the obfuscation has to comply with a specific criteria to provide anonymity, so that the data set could be safely released. This research contributes to: state the problems in the area; review related privacy and data protection legal requirements; discuss ontology-based obfuscation and anonymisation methods; and define relevant healthcare use cases. As a result, we present the early concept of our Ontology-based Data Sharing Service (O-DSS) that enforces patient’s privacy by means of obfuscation and anonymisation functions.

Citation: Iwaya, Leonardo H. and Giunchiglia, Fausto and Martucci, Leonardo A. and Hume, Alethia and Fischer-H{\”u}bner, Simone and Chenu-Abente, Ronald, “Ontology-Based Obfuscation and Anonymisation for Privacy”, In “Privacy and Identity Management. Time for a Revolution? 10th IFIP WG 9.2, 9.5, 9.6/11.7, 11.4, 11.6/SIG 9.2.2 International Summer School, Edinburgh, UK, August 16-21, 2015, Revised Selected Papers”, 2016, Springer International Publishing, Cham, pages 343–358, isbn 978-3-319-41763-9, doi 10.1007/978-3-319-41763-9_23, http://dx.doi.org/10.1007/978-3-319-41763-9_23. New York, USA, July 2016.

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Abstract: As the European Union (EU) funded SmartSociety project aims to create a toolset for rapidly and systematically engineering collective intelligence systems to support daily living, it simultaneously wants to ameliorate the risks to individuals of participating in these types of hyper-connected digital systems. This paper reports on a panel session at the close at of the 2015 IFIP summer school that reflected upon a keynote speech covering SmartSociety concepts, technologies and ethical dilemmas. The panel session was conceived as a consultative exercise as part of the ongoing Responsible Research and Innovation (RRI) approach embedded within the SmartSociety project. In this chapter we present an analysis of the panel session discussion, which touched on several key issues, including the relationships between technology and society, what we should expect from a ‘SmartSociety’, barriers and horizons in managing ethical issues, and brokerage as a methodological approach to weaving multiple perspectives into design.

Citation: Hartswood, Mark, and Marina Jirotka. “SmartSociety: Collaboration Between Humans and Machines, Promises and Perils.” In Privacy and Identity Management. Time for a Revolution?, Aspinall, D., Camenisch, J., Hansen, M., Fischer-Hübner, S.,Raab, C. (Eds.) pp. 30-48. Springer.

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Abstract: In this paper, we introduce a privacy-enhanced Peer Manager, which is a fundamental building block for the implementation of a privacy-preserving collective adaptive systems computing platform. The Peer Manager is a user-centered identity management platform that keeps information owned by a user private and is built upon an attribute based privacy policy. Furthermore, this paper explores the ethical, privacy and social values aspects of collective adaptive systems and their extensive capacity to transform lives. We discuss the privacy, social and ethical issues around profiles and present their legal privacy requirements from the European legislation perspective. © IFIP International Federation for Information Processing 2015.

Citation: Mark Hartswood, Marina Jirotka, Ronald Chenu-Abente, Alethia Hume, Fausto Giunchiglia, Leonardo A. Martucci, Simone Fischer-Hübner. “Privacy for Peer Profiling in Collective Adaptive Systems.” Privacy and Identity Management for the Future Internet in the Age of Globalisation. Springer International Publishing, 2014. 237-252.

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Abstract: Digital societies increasingly rely on secure communication between parties. Certificate enrollment protocols are used by certificate authorities to issue public key certificates to clients. Key agreement protocols, such as Diffie-Hellman, are used to compute secret keys, using public keys as input, for establishing secure communication channels. Whenever the keys are generated by clients, the bootstrap process requires either (a) an out-of-band verification for certification of keys when those are generated by the clients themselves, or (b) a trusted server to generate both the public and secret parameters. This paper presents a novel constrained key agreement protocol, built upon a constrained Diffie-Hellman, which is used to generate a secure public-private key pair, and to set up a certification environment without disclosing the private keys. In this way, the servers can guarantee that the generated key parameters are safe, and the clients do not disclose any secret information to the servers.

Citation: F{\’a}bio Borges and Leonardo A. Martucci and Filipe Beato and and Max M{\”u}hlh{\”a}user (2014). Secure and Privacy-Friendly Public Key Generation and Certification. In Proceedings of the 13th IEEE International Conference on Trust, Security and Privacy in Computing and Communications, 24–26 September, Beijing, China, TrustCom 2014.

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Abstract: Privacy-enhancing technologies for the Smart Grid usually address either the consolidation of users’ energy consumption or the verification of billing information. The goal of this paper is to introduce iKUP, a protocol that addresses both problems simultaneously. iKUP is an efficient privacy-enhancing protocol based on DC-Nets and Elliptic Curve Cryptography as Commitment. It covers the entire cycle of power provisioning, consumption, billing, and verification. iKUP allows: (i) utility providers to obtain a consolidated energy consumption value that relates to the consumption of a user set, (ii) utility providers to verify the correctness of this consolidated value, and (iii) the verification of the correctness of the billing information by both utility providers and users. iKUP prevents utility providers from identifying individual contributions to the consolidated value and, therefore, protects the users’ privacy. The analytical performance evaluation of iKUP is validated through simulation using as input a real-world data set with over 157 million measurements collected from 6,345 smart meters. Our results show that iKUP has a worse performance than other protocols in aggregation and decryption, which are operations that happen only once per round of measurements and, thus, have a low impact in the total protocol performance. iKUP heavily outperforms other protocols in encryption, which is the most demanded cryptographic function, has the highest impact on the overall protocol performance, and it is executed in the smart meters.

Citation: F{\’a}bio Borges and Leonardo A. Martucci (2014). {iKUP} Keeps Users’ Privacy in the Smart Grid. In Proceedings of the IEEE Conference on Communications and Network Security (CNS 2014), 29–31 Oct, San Francisco, CA, USA.

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Abstract: The impact of Social Collective Intelligent Systems (SCIS) on the individual right of privacy is discussed in this chapter under the light of the relevant privacy principles of the European Data Protection Legal Framework and the OECD Privacy Guidelines. This chapter analyzes the impact and limits of profiling, provenance and reputation on the right of privacy and review the legal privacy protection for profiles. From the technical perspective, we discuss opportunities and challenges for designing privacy-preserving systems for SCIS concerning collectives and decentralized systems. Furthermore, we present a selection of privacy-enhancing technologies that are relevant for SCIS including anonymous credentials, transparency-enhancing tools and the PrimeLife Policy Language (PPL) and discuss how these technologies can help to enforce the main legal principles of the European Data Protection Legal Framework.

Citation: Fischer-Hübner, S. and Martucci, L. A., “Privacy in Social Collective Intelligence Systems”, in Miorandi, D., Maltese, V., Rovatsos, M., Nijholt., A. and Stewart, J. (eds) Social collective intelligence: Combining the powers of humans and machines Springer, 2014.

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This work was presented at HAIDM 2015. The 2015 workshop on Human-Agent Interaction Design and Models was co-organised by SmartSociety.

Abstract: It can be hard to automatically identify sensitive content in images or other media because significant context is often necessary to interpret noisy content and complex notions of sensitivity. Online crowds can help computers interpret information that cannot be understood algorithmically. However, systems that use this approach can unwittingly show workers information that should remain private. For instance, images sent to the crowd may accidentally include faces or geographic identifiers in the background, and information pertaining to a task (e.g., the amount of a bill) may appear alongside private information (e.g., an account number). This paper introduces an approach for using crowds to filter information from sensory data that should remain private, while retaining information needed to complete a specified task. The pyramid workflow that we introduce allows crowd workers to identify private information while never having complete access to the (potentially private) information they are filtering. Our approach is flexible, easily configurable, and can protect user information in settings where automated approaches fail. Our experiments with 4685 crowd workers show that it performs significantly better than previous approaches.

Citation: Walter Lasecki, Mitchell Gordon, Jaime Teevan, Ece Kamar and Jeffrey Bigham. CrowdMask: Privacy-Preserving Crowd-Powered Systems.

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Social computing broadly refers to computing-supported approaches that facilitate interactions among people and organizations. Social computing has emerged as an exciting multidisciplinary area of research, driven by the wealth of easily available information and the success of online social networks and social media. Social computing applications are characterized by high interactivity among users, user-generated content, and in cases such as Wikipedia, more open governance structures. Much of the recent excitement in social computing is driven by data analytics and business models. What is still lacking, however, is a deeper conceptual understanding of social computing — e.g., relating to its conceptual bases, information and abstractions, design principles, and platforms. This event invites papers that take an explicitly multiagent perspective in addressing these gaps and do so in thought-provoking ways.

The First International Workshop on Multiagent Foundations of Social Computing is Co-located with AAMAS 2014

Important Dates

• Submission: January 22, 2014
• Notification: February 19, 2014
• Camera-ready due: March 5, 2014