ÐMicroaggregation is a statistical disclosure control technique for microdata disseminated in statistical databases. Raw microdata (i.e., individual records or data vectors) are grouped into small aggregates prior to publication. Each aggregate should contain at least k data vectors to prevent disclosure of individual information, where k is a constant value preset by the data protector. No exact polynomial algorithms are known to date to microaggregate optimally, i.e., with minimal variability loss. Methods in the literature rank data and partition them into groups of fixed-size; in the multivariate case, ranking is performed by projecting data vectors onto a single axis. In this paper, candidate optimal solutions to the multivariate and univariate microaggregation problems are characterized. In the univariate case, two heuristics based on hierarchical clustering and genetic algorithms are introduced which are data-oriented in that they try to preserve natural data aggregates. In the multivariate case, fixed-size and hierarchical clustering microaggregation algorithms are presented which do not require data to be projected onto a single dimension; such methods clearly reduce variability loss as compared to conventional multivariate microaggregation on projected data. Index TermsÐStatistical databases, microdata protection, statistical disclosure control, microaggregation, hierarchical clustering, genetic algorithms. ae 1 INTRODUCTION S TATISTICAL offices release two kinds of data through their statistical databases: tabular data and microdata sets (individual respondent records). In both cases, data dissemination should be performed in a way that does not lead to disclosure of individual information, but preserves the informational content as much as possible (disclosure control problem). While there is long experience in table dissemination, microdata dissemination is a much more recent activity. Following the nomenclature of [24], a microdata set is a set of records or data vectors containing data of individual respondents who can be persons, companies, etc. The individual data vectors of a microdata set are stored in a microdata file. Each individual j is assigned a data vector V j formed by two kinds of variables: Key variables: These variables are identifiers of the individual. A key variable can be a direct identifier (e.g., the name) or an indirect identifier (e.g., the phone number). Sensitive variables: They contain sensitive information on the individual (e.g., salary). Key variables allow identification of the individual. If a direct identifier is known by the intruder, then he can uniquely identify a specific individual without additional

k-Anonymity is a useful concept to solve the tension between data utility and respondent privacy in individual data (microdata) protection. However, the generalization and suppression approach proposed in the literature to achieve k-anonymity is not equally suited for all types of attributes: (i) generalization/suppression is one of the few possibilities for nominal categorical attributes; (ii) it is just one possibility for ordinal categorical attributes which does not always preserve ordinality; (iii) and it is completely unsuitable for continuous attributes, as it causes them to lose their numerical meaning. Since attributes leading to disclosure (and thus needing kanonymization) may be nominal, ordinal and also continuous, it is important to devise k-anonymization procedures which preserve the semantics of each attribute type as much as possible. We propose in this paper to use categorical microaggregation as an alternative to generalization/suppression for nominal and ordinal k-anonymization; we also propose continuous microaggregation as the method for continuous k-anonymization.

Existing authentication protocols to secure vehicular ad hoc networks (VANETs) raise challenges such as certificate distribution and revocation, avoidance of computation and communication bottlenecks, and reduction of the strong reliance on tamper-proof devices. This paper efficiently copes with these challenges with a decentralized group-authentication protocol in the sense that the group is maintained by each roadside unit (RSU) rather than by a centralized authority, as in most existing protocols that are employing group signatures. In our proposal, we employ each RSU to maintain and manage an on-the-fly group within its communication range. Vehicles entering the group can anonymously broadcast vehicle-to-vehicle (V2V) messages, which can be instantly verified by the vehicles in the same group (and neighboring groups). Later, if the message is found to be false, a third party can be invoked to disclose the identity of the message originator. Our protocol efficiently exploits the specific features of vehicular mobility, physical road limitations, and properly distributed RSUs. Our design leads to a robust VANET since, if some RSUs occasionally collapse, only the vehicles that are driving in those collapsed areas will be affected. Due to the numerous RSUs sharing the load to maintain the system, performance does not significantly degrade when more vehicles join the VANET; hence, the system is scalable. Disciplines Physical Sciences and Mathematics Publication DetailsZhang, L., Wu, Q., Solanas, A. & Domingo-Ferrer, J. (2010) Abstract-Existing authentication protocols to secure vehicular ad hoc networks (VANETs) raise challenges such as certificate distribution and revocation, avoidance of computation and communication bottlenecks, and reduction of the strong reliance on tamper-proof devices. This paper efficiently copes with these challenges with a decentralized group-authentication protocol in the sense that the group is maintained by each roadside unit (RSU) rather than by a centralized authority, as in most existing protocols that are employing group signatures. In our proposal, we employ each RSU to maintain and manage an on-the-fly group within its communication range. Vehicles entering the group can anonymously broadcast vehicle-to-vehicle (V2V) messages, which can be instantly verified by the vehicles in the same group (and neighboring groups). Later, if the message is found to be false, a third party can be invoked to disclose the identity of the message originator. Our protocol efficiently exploits the specific features of vehicular mobility, physical road limitations, and properly distributed RSUs. Our design leads to a robust VANET since, if some RSUs occasionally collapse, only the vehicles that are driving in those collapsed areas will be affected. Due to the numerous RSUs sharing the load to maintain the system, performance does not significantly degrade when more vehicles join the VANET; hence, the system is scalable.Index Terms-Conditional privacy, information security, protocol design, v...

Abstract-t-Closeness is a privacy model recently defined for data anonymization. A data set is said to satisfy t-closeness if, for each group of records sharing a combination of key attributes, the distance between the distribution of a confidential attribute in the group and the distribution of the attribute in the entire data set is no more than a threshold t. Here, we define a privacy measure in terms of information theory, similar to t-closeness. Then, we use the tools of that theory to show that our privacy measure can be achieved by the postrandomization method (PRAM) for masking in the discrete case, and by a form of noise addition in the general case.

A group key agreement (GKA) protocol allows a set of users to establish a common secret via open networks. Observing that a major goal of GKAs for most applications is to establish a confidential channel among group members, we revisit the group key agreement definition and distinguish the conventional (symmetric) group key agreement from asymmetric group key agreement (ASGKA) protocols. Instead of a common secret key, only a shared encryption key is negotiated in an ASGKA protocol. This encryption key is accessible to attackers and corresponds to different decryption keys, each of which is only computable by one group member. We propose a generic construction of one-round ASGKAs based on a new primitive referred to as aggregatable signaturebased broadcast (ASBB), in which the public key can be simultaneously used to verify signatures and encrypt messages while any signature can be used to decrypt ciphertexts under this public key. Using bilinear pairings, we realize an efficient ASBB scheme equipped with useful properties. Following the generic construction, we instantiate a one-round ASGKA protocol tightly reduced to the decision Bilinear Diffie-Hellman Exponentiation (BDHE) assumption in the standard model.