Search

Data explicitly stored in a temporal database are often associated with certain semantic assumptions. Each assumption can be viewed as a way of deriving implicit information from explicitly stored data. Rather than leaving the task of deriving (possibly infinite) implicit data to application programs, as is the case currently, it is desirable that this be handled by the database management system. To achieve this, this paper formalizes and studies two types of semantic assumptions: point-based and interval-based. The point-based assumptions include those assumptions that use interpolation methods over values at different time instants, while the interval-based assumptions include those that involve the conversion of values across different time granularities. The paper presents techniques on: 1) how assumptions on specific sets of attributes can be automatically derived from the specification of interpolation and conversion functions, and 2) given the representation of assumptions, how a user query can be converted into a system query such that the answer of this system query over the explicit data is the same as that of the user query over the explicit and the implicit data, To precisely illustrate concepts and algorithms, the paper uses a logic-based abstract query language. The paper also shows how the same concepts can be applied to concrete temporal query languages. Index Terms - Temporal databases, time granularity, temporal query languages, implicit data, TSQL2.

The purpose of good database logical design is to eliminate data redundancy and insertion and deletion anomalies. In order to achieve this objective for temporal databases, the notions of temporal […]

This paper presents a discretionary access control model in which authorizations contain temporal intervals of validity. An authorization is automatically revoked when the associated temporal interval expires. The proposed model provides rules for the automatic derivation of new authorizations from those explicitly specified. Both positive and negative authorizations are supported. A formal definition of those concepts is presented in the paper, together with the semantic interpretation of authorizations and derivation rules as clauses of a general logic program. Issues deriving from the presence of negative authorizations are discussed. We also allow negation in rules: it is possible to derive new authorizations on the basis of the absence of other authorizations. The presence of this type of rules may lead to the generation of different sets of authorizations, depending on the evaluation order. An approach is presented, based on establishing an ordering among authorizations and derivation rules, which guarantees a unique set of valid authorizations. Moreover, we give an algorithm detecting whether such an ordering can be established for a given set of authorizations and rules. Administrative operations for adding, removing, or modifying authorizations and derivation rules are presented and efficiency issues related to these operations are also tackled in the paper. A materialization approach is proposed, allowing to efficiently perform access control. Index Terms - Database security, temporal authorization, database management, temporal reasoning, general logic programs, access control.

One of the major challenges in Human Activity Recognition (HAR) based on machine learning is the scarcity of labeled data. Indeed, collecting a sufficient amount of training data to build a reliable recognition problem is often prohibitive. Among the many solutions in the literature to mitigate this issue, collaborative learning is emerging as a promising direction to distribute the annotation burden over multiple users that cooperate to build a shared recognition model. One of the major issues of existing methods is that they assume a static activity model with a fixed set of target activities. In this paper, we propose a novel approach that is based on Growing When Required (GWR) neural networks. A GWR network continuously adapts itself according to the input training data, and hence it is particularly suited when the users share heterogeneous sets of activities. Like in federated learning, for the sake of privacy preservation, each user contributes to the global activity classifier by sharing personal model parameters, and not by directly sharing data. In order to further mitigate privacy threats, we implement a strategy to avoid releasing model parameters that may indirectly reveal information about activities that the user specifically marked as private. Our results on two well-known publicly available datasets show the effectiveness and the flexibility of our approach. [ABSTRACT FROM AUTHOR]

Recognition of activities of daily living (ADLs) is an enabling technology for several ubiquitous computing applications. Most activity recognition systems rely on supervised learning to extract activity models from labeled datasets. A problem with that approach is the acquisition of comprehensive activity datasets, which is an expensive task. The problem is particularly challenging when focusing on complex ADLs characterized by large variability of execution. Moreover, several activity recognition systems are limited to offline recognition, while many applications claim for online activity recognition. In this paper, we propose POLARIS, a framework for unsupervised activity recognition. POLARIS can recognize complex ADLs exploiting the semantics of activities, context data, and sensors. Through ontological reasoning, our algorithm derives semantic correlations among activities and sensor events. By matching observed events with semantic correlations, a statistical reasoner formulates initial hypotheses about the occurred activities. Those hypotheses are refined through probabilistic reasoning, exploiting semantic constraints derived from the ontology. Our system supports online recognition, thanks to a novel segmentation algorithm. Extensive experiments with real-world datasets show that the accuracy of our unsupervised method is comparable to the one of supervised approaches. Moreover, the online version of our system achieves essentially the same accuracy of the offline version. [ABSTRACT FROM AUTHOR]

Privacy legislation has often been identified as a roadblock for advanced context-aware applications. The feedback collected from more than 150 researchers in pervasive computing reveals a different attitude. Has pervasive computing?s privacy challenge been solved? [ABSTRACT FROM AUTHOR]

Cyber-security systems, which protect networks and computers against cyber attacks, are becoming common due to increasing threats and government regulation. At the same time, the enormous amount of data gathered by cyber-security systems poses a serious threat to the privacy of the people protected by those systems. To ground this threat, we survey common and novel cyber-security technologies and analyze them according to the potential for privacy invasion. We suggest a taxonomy for privacy risks assessment of information security technologies, based on the level of data exposure, the level of identification of individual users, the data sensitivity and the user control over the monitoring, and collection and analysis of the data. We discuss our results in light of the recent technological trends and suggest several new directions for making these mechanisms more privacy-aware. [ABSTRACT FROM AUTHOR]

Copyright of Anuario do Instituto de Geociencias is the property of Universidade Federal do Rio de Janeiro, Instituto de Geociencias and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Large datasets of real network flows acquired from the Internet are an invaluable resource for the research community. Applications include network modeling and simulation, identification of security attacks, and validation of research results. Unfortunately, network flows carry extremely sensitive information, and this discourages the publication of those datasets. Indeed, existing techniques for network flow sanitization are vulnerable to different kinds of attacks, and solutions proposed for microdata anonymity cannot be directly applied to network traces. In our previous research, we proposed an obfuscation technique for network flows, providing formal confidentiality guarantees under realistic assumptions about the adversary's knowledge. In this paper, we identify the threats posed by the incremental release of network flows, we propose a novel defense algorithm, and we formally prove the achieved confidentiality guarantees. An extensive experimental evaluation of the algorithm for incremental obfuscation, carried out with billions of real Internet flows, shows that our obfuscation technique preserves the utility of flows for network traffic analysis. [ABSTRACT FROM PUBLISHER]

Access control models, such as the ones supported by commercial DBMSs, are not yet able to fully meet many application needs. An important requirement derives from the temporal dimension that […]

A privacy violation occurs when the association between an individual identity and data considered private by that individual is obtained by an unauthorized party. Uncertainty and indistinguishability are two independent aspects that characterize the degree of this association being revealed. Indistinguishability refers to the property that the attacker cannot see the difference among a group of individuals, while uncertainty refers to the property that the attacker cannot tell which private value, among a group of values, an individual actually has. This paper investigates the notion of indistinguishability as a general form of anonymity, applicable, for example, not only to generalized private tables, but to relational views and to sets of views obtained by multiple queries over a private database table. It is shown how indistinguishability is highly influenced by certain symmetries among individuals, in the released data, with respect to their private values. The paper provides both theoretical results and practical algorithms for checking if a specific set of views over a private table provide sufficient indistinguishability. [ABSTRACT FROM AUTHOR]

Spatial generalisation has been recently proposed as a technique for the anonymisation of requests in location based services. This article provides a formal characterisation of a privacy attack that has been informally described in previous work, and presents a new generalisation algorithm that is proved to be a safe defense against that attack. The article also reports the results of an extensive experimental study, comparing the new algorithm with the ones that have been previously proposed in the literature. [ABSTRACT FROM AUTHOR]

In the recent years several research efforts have focused on the concept of time granularity and its applications. A first stream of research investigated the mathematical models behind the notion of granularity and the algorithms to manage temporal data based on those models. A second stream of research investigated symbolic formalisms providing a set of algebraic operators to define granularities in a compact and compositional way. However, only very limited manipulation algorithms have been proposed to operate directly on the algebraic representation making it unsuitable to use the symbolic formalisms in applications that need manipulation of granularities. This paper aims at filling the gap between the results from these two streams of research, by providing an efficient conversion from the algebraic representation to the equivalent low-level representation based on the mathematical models. In addition, the conversion returns a minimal representation in terms of period length. Our results have a major practical impact: users can more easily define arbitrary granularities in terms of algebraic operators, and then access granularity reasoning and other services operating efficiently on the equivalent, minimal low-level representation. As an example, we illustrate the application to temporal constraint reasoning with multiple granularities. From a technical point of view, we propose an hybrid algorithm that interleaves the conversion of calendar subexpressions into periodical sets with the minimization of the period length. The algorithm returns set-based granularity representations having minimal period length, which is the most relevant parameter for the performance of the considered reasoning services. Extensive experimental work supports the techniques used in the algorithm, and shows the efficiency and effectiveness of the algorithm. [ABSTRACT FROM AUTHOR]

Analyzes the discovery of frequent event patterns with multiple granularities in time sequences. Information on temporal constraints with granularities; Details on the temporal pattern-discovery problem; Techniques for an effective discovery process; Reduction of candidate complex event types.

Web queries, credit card transactions, and medical records are examples of transaction data flowing in corporate data stores, and often revealing associations between individuals and sensitive information. The serial release of these data to partner institutions or data analysis centers in a nonaggregated form is a common situation. In this paper, we show that correlations among sensitive values associated to the same individuals in different releases can be easily used to violate users' privacy by adversaries observing multiple data releases, even if state-of-the-art privacy protection techniques are applied. We show how the above sequential background knowledge can be actually obtained by an adversary, and used to identify with high confidence the sensitive values of an individual. Our proposed defense algorithm is based on Jensen-Shannon divergence; experiments show its superiority with respect to other applicable solutions. To the best of our knowledge, this is the first work that systematically investigates the role of sequential background knowledge in serial release of transaction data. [ABSTRACT FROM AUTHOR]

Geo-social networks (GeoSNs) provide context-aware services that help associate location with users and content. The proliferation of GeoSNs indicates that they're rapidly attracting users. GeoSNs currently offer different types of services, including photo sharing, friend tracking, and "check-ins." However, this ability to reveal users' locations causes new privacy threats, which in turn call for new privacy-protection methods. The authors study four privacy aspects central to these social networks — location, absence, co-location, and identity privacy — and describe possible means of protecting privacy in these circumstances. [ABSTRACT FROM AUTHOR]