Exploiting contextual information in attacking set-generalized transactions

Transactions are records that contain a set of items about individuals. For example, items browsed by a customer when shopping online form a transaction. Today, many activities are carried out on the Internet, resulting in a large amount of transaction data being collected. Such data are often shared and analyzed to improve business and services, but they also contain private information about individuals that must be protected. Techniques have been proposed to sanitize transaction data before their release, and set-based generalization is one such method. In this article, we study how well set-based generalization can protect transactions. We propose methods to attack set-generalized transactions by exploiting contextual information that is available within the released data. Our results show that set-based generalization may not provide adequate protection for transactions, and up to 70% of the items added into the transactions during generalization to obfuscate original data can be detected by our methods with a precision over 80%

Zerber+R: Top-k Retrieval from a Confidential Index

Zerr, S., Olmedilla, D., Nejdl, W., & Siberski, W. (2009). Zerber+R: Top-k Retrieval from a Confidential Index. Proceedings of the 12th International Conference on Extending Database Technology: Advances in Database Technology (pp. 439-449). March, 24-26, 2009, Saint Petersburg, Russia (ISBN: 978-1-60558-422-5).Privacy-preserving document exchange among collaboration groups in an enterprise as well as across enterprises requires techniques for sharing and search of access-controlled information through largely untrusted servers. In these settings search systems need to provide confidentiality guarantees for shared information while offering IR properties comparable to the ordinary search engines. Top-k is a standard IR technique which enables fast query execution on very large indexes and makes systems highly scalable. However, indexing access-controlled information for top-k retrieval is a challenging task due to the sensitivity of the term statistics used for ranking. In this paper we present Zerber+R -- a ranking model which allows for privacy-preserving top-k retrieval from an outsourced inverted index. We propose a relevance score transformation function which makes relevance scores of different terms indistinguishable, such that even if stored on an untrusted server they do not reveal information about the indexed data. Experiments on two real-world data sets show that Zerber+R makes economical usage of bandwidth and offers retrieval properties comparable with an ordinary inverted index.The work on this publication has been sponsored by the TENCompetence Integrated Project that is funded by the European Commission's 6th Framework Programme, priority IST/Technology Enhanced Learning. Contract 027087 [http://www.tencompetence.org

Tunable Privacy for Access Controlled Data in Peer-to-Peer Systems

Peer-to-peer paradigm is increasingly employed for organizing distributed resources for various applications, e.g. content distribution, open storage grid etc. In open environments, even when proper access control mechanisms supervise the access to the resources, privacy issues may arise depending on the application. In this paper, we introduce, PANACEA, a system that offers high and tunable privacy based on an innovative resource indexing approach. In our case, privacy has two aspects: the deducibility of a resource's existence/non-existence and the discovery of the provider of the resource. We systematically study the privacy that can be provided by the proposed system and compare its effectiveness as related to conventional P2P systems. Employing both probabilistic and information-theoretic approaches, we analytically derive that PANACEA can offer high privacy, while preserving high search efficiency for authorized users. Our analysis and the effectiveness of the approach have been experimentally verified. Moreover, the privacy offered by the proposed system can be tuned according to the specific application needs which is illustrated with detailed simulation study

PANACEA: Tunable Privacy for Access Controlled Data in Peer-to-Peer Systems

Peer-to-peer paradigm is increasingly employed for organizing distributed resources for various applications, e.g. content distribution, open storage grid etc. In open environments, even when proper access control mechanisms supervise the access to the resources, privacy issues may arise depending on the application. In this paper, we introduce, PANACEA, a system that offers high and tunable privacy based on an innovative resource indexing approach. In our case, privacy has two aspects: the deduceability of a resource's existence/non-existence and the discovery of the provider of the resource. We systematically study the privacy that can be provided by the proposed system and compare its effectiveness as related to conventional P2P systems. Employing both probabilistic and information-theoretic approaches, we analytically derive that PANACEA can offer high privacy, while preserving high search efficiency for authorized users. Our analysis and the effectiveness of the approach have been experimentally verified. Moreover, the privacy offered by the proposed system can be tuned according to the specific application needs which is illustrated with detailed simulation study

Privacy and Ownership Preserving of Outsourced Medical Data

The demand for the secondary use of medical data is increasing steadily to allow for the provision of better quality health care. Two important issues pertaining to this sharing of data have to be addressed: one is the privacy protection for individuals referred to in the data; the other is copyright protection over the data. In this paper, we present a unified framework that seamlessly combines techniques of binning and digital watermarking to attain the dual goals of privacy and copyright protection. Our binning method is built upon an earlier approach of generalization and suppression by allowing a broader concept of generalization. To ensure data usefulness, we propose constraining Binning by usage metrics that define maximal allowable information loss, and the metrics can be enforced off-line. Our watermarking algorithm watermarks the binned data in a hierarchical manner by leveraging on the very nature of the data. The method is resilient to the generalization attack that is specific to the binned data, as well as other attacks intended to destroy the inserted mark. We prove that watermarking could not adversely interfere with binning, and implemented the framework. Experiments were conducted, and the results show the robustness of the proposed framework

Towards ACPeer: an Access Control aware P2P System

P2P data management systems provide a scalable alternative to centralized architectures. Their adoption, however, is limited by the lack of possibility to control the access on the resources stored in the system. We address the problem of access control for structured P2P systems and introduce various techniques to realize controlled access to the resources. We focus mainly on the case of non-mutable data objects, and however, some of the techniques introduced can handle the case of mutable objects straight away. In particular, we are interested to realize access control when the P2P data management system is used in a collaborative use case scenario. We explore the solution space elaborately and present solution approaches which realize the planned systems by either constructing independent networks or enforcing the access control at querying time or at replying time

Privacy-Preserving Similarity-Based Text Retrieval

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Scalable discovery of networked data : Algorithms, Infrastructure, Applications

Harmelen, F.A.H. van [Promotor]Siebes, R.M. [Copromotor