Milan Petkovic

Bio: Milan Petkovic is an academic researcher from Eindhoven University of Technology. The author has contributed to research in topics: Encryption & Access control. The author has an hindex of 31, co-authored 174 publications receiving 3261 citations. Previous affiliations of Milan Petkovic include Philips & University of Twente.

Making sense of big data in health research: Towards an EU action plan

Abstract: Medicine and healthcare are undergoing profound changes. Whole-genome sequencing and high-resolution imaging technologies are key drivers of this rapid and crucial transformation. Technological innovation combined with automation and miniaturization has triggered an explosion in data production that will soon reach exabyte proportions. How are we going to deal with this exponential increase in data production? The potential of “big data” for improving health is enormous but, at the same time, we face a wide range of challenges to overcome urgently. Europe is very proud of its cultural diversity; however, exploitation of the data made available through advances in genomic medicine, imaging, and a wide range of mobile health applications or connected devices is hampered by numerous historical, technical, legal, and political barriers. European health systems and databases are diverse and fragmented. There is a lack of harmonization of data formats, processing, analysis, and data transfer, which leads to incompatibilities and lost opportunities. Legal frameworks for data sharing are evolving. Clinicians, researchers, and citizens need improved methods, tools, and training to generate, analyze, and query data effectively. Addressing these barriers will contribute to creating the European Single Market for health, which will improve health and healthcare for all Europeans.

Mediated Ciphertext-Policy Attribute-Based Encryption and its Application (extended version)

Abstract: In Ciphertext-Policy Attribute-Based Encryption (CP-ABE), a user secret key is associated with a set of attributes, and the ciphertext is associated with an access policy over attributes. The user can decrypt the ciphertext if and only if the attribute set of his secret key satisfies the access policy specified in the ciphertext. Several CP-ABE schemes have been proposed, however, some practical problems, such as attribute revocation, still needs to be addressed. In this paper, we propose a mediated Ciphertext-Policy Attribute-Based Encryption (mCP-ABE) which extends CP-ABE with instantaneous attribute revocation. Furthermore, we demonstrate how to apply the proposed mCP-ABE scheme to securely manage Personal Health Records (PHRs).

Mediated Ciphertext-Policy Attribute-Based Encryption and Its Application

Abstract: In Ciphertext-Policy Attribute-Based Encryption (CP-ABE), a user secret key is associated with a set of attributes, and the ciphertext is associated with an access policy over attributes. The user can decrypt the ciphertext if and only if the attribute set of his secret key satisfies the access policy specified in the ciphertext. Several CP-ABE schemes have been proposed, however, some practical problems, such as attribute revocation, still needs to be addressed. In this paper, we propose a mediated Ciphertext-Policy Attribute-Based Encryption (mCP-ABE) which extends CP-ABE with instantaneous attribute revocation. Furthermore, we demonstrate how to apply the proposed mCP-ABE scheme to securely manage Personal Health Records (PHRs).

Secure Management of Personal Health Records by Applying Attribute-Based Encryption

Abstract: The confidentiality of personal health records is a major problem when patients use commercial Web-based systems to store their health data. Traditional access control mechanisms, such as Role-Based Access Control, have several limitations with respect to enforcing access control policies and ensuring data confidentiality. In particular, the data has to be stored on a central server locked by the access control mechanism, and the data owner loses control on the data from the moment when the data is sent to the requester. Therefore, these mechanisms do not fulfil the requirements of data outsourcing scenarios where the third party storing the data should not have access to the plain data, and it is not trusted to enforce access control policies. In this paper, we describe a new approach which enables secure storage and controlled sharing of patient’s health records in the aforementioned scenarios. A new variant of a ciphertext-policy attribute-based encryption scheme is proposed to enforce patient/organizational access control policies such that everyone can download the encrypted data but only authorized users from the social domain (e.g. family, friends, or fellow patients) or authorized users from the professional domain (e.g. doctors or nurses) are allowed to decrypt it.

Secure management of personal health records by applying attribute-based encryption

Abstract: The confidentiality of personal health records is a major problem when patients use commercial Web-based systems to store their health data. Traditional access control mechanisms have several limitations with respect to enforcing access control policies and ensuring data confidentiality. In particular, the data has to be stored on a central server locked by the access control mechanism, and the data owner loses control on the data from the moment when the data is sent to the server. Therefore, these mechanisms do not fulfill the requirements of data outsourcing scenarios where the third party storing the data should not have access to the plain data, and it is not trusted to enforce access policies. In this paper, we present a new variant of ciphertext-policy attribute-based encryption (CP-ABE) scheme which is used to enforce patient/organizational access control policies. In CP-ABE, the data is encrypted according to an access policy over a set of attributes. The access policy specifies which attributes a user needs to have in order to decrypt the encrypted data. Once the data is encrypted, it can be safely stored in an untrusted server such that everyone can download the encrypted data but only authorized users who satisfy the access policy can decrypt. The novelty of our construction is that attributes can be from two security domains: social domain (e.g. family, friends, or fellow patients) and professional domain (e.g. doctors or nurses).

Machine learning

Abstract: Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Scalable and Secure Sharing of Personal Health Records in Cloud Computing Using Attribute-Based Encryption

Abstract: Personal health record (PHR) is an emerging patient-centric model of health information exchange, which is often outsourced to be stored at a third party, such as cloud providers. However, there have been wide privacy concerns as personal health information could be exposed to those third party servers and to unauthorized parties. To assure the patients' control over access to their own PHRs, it is a promising method to encrypt the PHRs before outsourcing. Yet, issues such as risks of privacy exposure, scalability in key management, flexible access, and efficient user revocation, have remained the most important challenges toward achieving fine-grained, cryptographically enforced data access control. In this paper, we propose a novel patient-centric framework and a suite of mechanisms for data access control to PHRs stored in semitrusted servers. To achieve fine-grained and scalable data access control for PHRs, we leverage attribute-based encryption (ABE) techniques to encrypt each patient's PHR file. Different from previous works in secure data outsourcing, we focus on the multiple data owner scenario, and divide the users in the PHR system into multiple security domains that greatly reduces the key management complexity for owners and users. A high degree of patient privacy is guaranteed simultaneously by exploiting multiauthority ABE. Our scheme also enables dynamic modification of access policies or file attributes, supports efficient on-demand user/attribute revocation and break-glass access under emergency scenarios. Extensive analytical and experimental results are presented which show the security, scalability, and efficiency of our proposed scheme.

Video abstraction: A systematic review and classification

Abstract: The demand for various multimedia applications is rapidly increasing due to the recent advance in the computing and network infrastructure, together with the widespread use of digital video technology. Among the key elements for the success of these applications is how to effectively and efficiently manage and store a huge amount of audio visual information, while at the same time providing user-friendly access to the stored data. This has fueled a quickly evolving research area known as video abstraction. As the name implies, video abstraction is a mechanism for generating a short summary of a video, which can either be a sequence of stationary images (keyframes) or moving images (video skims). In terms of browsing and navigation, a good video abstract will enable the user to gain maximum information about the target video sequence in a specified time constraint or sufficient information in the minimum time. Over past years, various ideas and techniques have been proposed towards the effective abstraction of video contents. The purpose of this article is to provide a systematic classification of these works. We identify and detail, for each approach, the underlying components and how they are addressed in specific works.

Some Preliminary Comments on the DIRECTIVE 95/46/EC OF THE EUROPEAN PARLIAMENT AND OF THE COUNCIL of 24 October 1995 on the protection of individuals with regard to the processing of personal data and on the free movement of such data.

Abstract: (1) Whereas the objectives of the Community, as laid down in the Treaty, as amended by the Treaty on European Union, include creating an ever closer union among the peoples of Europe, fostering closer relations between the States belonging to the Community, ensuring economic and social progress by common action to eliminate the barriers which divide Europe, encouraging the constant improvement of the living conditions of its peoples, preserving and strengthening peace and liberty and promoting democracy on the basis of the fundamental rights recognized in the constitution and laws of the Member States and in the European Convention for the Protection of Human Rights and Fundamental Freedoms;