14146996
Cryptographic access control in a distributed file system
Traditional access control mechanisms rely on a reference monitor to mediate access to protected resources. Reference monitors are inherently centralized and existing attempts to distribute the functionality of the reference monitor suffer from problems of scalability.Cryptographic access control is a new distributed access control paradigm designed for a global federation of information systems. It defines an implicit access control mechanism, which relies exclusively on cryptography to provide confidentiality and integrity of data managed by the system. It is particularly designed to operate in untrusted environments where the lack of global knowledge and control are defining characteristics.The proposed mechanism has been implemented in a distributed file system, which is presented in this paper along with a preliminary evaluation of the proposed mechanism.
2275333
Risk probability estimating based on clustering
Ubiquitous computing environments are highly dynamic, with new unforeseen circumstances and constantly changing environments, which introduces new risks that cannot be assessed through traditional means of risk analysis. Mobile entities in a ubiquitous computing environment require the ability to perform an autonomous assessment of the risk incurred by a specific interaction with another entity in a given context. This assessment will allow a mobile entity to decide whether sufficient evidence exists to mitigate the risk and allow the interaction to proceed. Such evidence might include records of prior experiences, recommendations from a trusted entity or the reputation of the other entity. We propose a dynamic mechanism for estimating the risk probability of a certain interaction in a given environment using hybrid neural networks. We argue that traditional risk assessment models from the insurance industry do not directly apply to ubiquitous computing environments. Instead, we propose a dynamic mechanism for risk assessment, which is based on pattern matching, classification and prediction procedures. This mechanism uses an estimator of risk probability, which is based on the automatic clustering of defining features of the environment and the other entity, which helps avoid subjective judgments as much as possible.
18687186
Trading Privacy for Trust
Both privacy and trust relate to knowledge about an entity. However, there is an inherent conflict between trust and privacy: the more knowledge a first entity knows about a second entity, the more accurate should be the trustworthiness assessment; the more knowledge is known about this second entity, the less privacy is left to this entity. This conflict needs to be addressed because both trust and privacy are essential elements for a smart working world. The solution should allow the benefit of adjunct trust when entities interact without too much privacy loss. We propose to achieve the right trade-off between trust and privacy by ensuring minimal trade of privacy for the required trust. We demonstrate how transactions made under different pseudonyms can be linked and careful disclosure of such links fulfils this right trade-off.
7607582
Trust enhanced ubiquitous payment without too much privacy loss
Computational models of trust have been proposed for use in ubicomp environments for deciding whether to allow customers to pay with an e-purse or not. In order to build trust in a customer, a means to link transactions using the same e-purse is required. Roughly, trust is a result of knowledge. As the number of transactions increases, the resulting increase in knowledge about the user of the e-purse threatens privacy due to global profiling. We present a scheme (and its prototype) that mitigates this loss of privacy without forbidding the use of trust for smoothing payment by giving the opportunity to the user to divide trust (i.e. transactions) according to context (e.g. location, user's current activity or subset of shops).
7815359
Trust Transfer: Encouraging Self-recommendations Without Sybil Attack
Trading privacy for trust thanks to the linkage of pseudonyms has been proposed to mitigate the inherent conflict between trust and privacy. This necessitates fusionym, that is, the calculation of a unique trust value supposed to reflect the overall trustworthiness brought by the set of linked pseudonyms. In fact, some pieces of evidence may overlap and be overcounted, leading to an incorrect trust value. In this approach, self-recommendations are possible during the privacy/trust trade. However, this means that Sybil attacks, where thousands of virtual identities belonging to the same real-world entity recommend each other, are potentially easier to carry out, as self-recommendations are an integral part of the attack. In this paper, trust transfer is used to achieve safe fusionym and protect against Sybil attacks when pieces of evidence are limited to direct observations and recommendations based on the count of event outcomes. Trust transfer implies that recommendations move some of the trustworthiness of the recommending entity to the trustworthiness of the trustee. It is demonstrated and tailored to email anti-spam settings.
34386518
Privacy Recovery with Disposable Email Addresses
Disposable e-mail address (DEA) services are a privacy recovery mechanism for the growing spam problem. However, this problem is clearly more complex than simply closing DEA; as the rolling e-mail address protocols (REAP) system demonstrates, recovery approaches must accommodate different normal states.
16055581
Efficient evaluation of the valid-time natural join
Joins are arguably the most important relational operators. Poor implementations are tantamount to computing the Cartesian product of the input relations. In a temporal database, the problem is more acute for two reasons. First, conventional techniques are designed for the optimization of joins with equality predicates, rather than the inequality predicates prevalent in valid-time queries. Second, the presence of temporally-varying data dramatically increases the size of the database. These factors require new techniques to efficiently evaluate valid-time joins. The authors address this need for efficient join evaluation in databases supporting valid-time. A new temporal-join algorithm based on tuple partitioning is introduced. This algorithm avoids the quadratic cost of nested-loop evaluation methods; it also avoids sorting. Performance comparisons between the partition-based algorithm and other evaluation methods are provided. While the paper focuses on the important valid-time natural join, the techniques presented are also applicable to other valid-time joins.<<ETX>>
13866013
A foundation for capturing and querying complex multidimensional data
Abstract On-line analytical processing (OLAP) systems considerably improve data analysis and are finding wide-spread use. OLAP systems typically employ multidimensional data models to structure their data. This paper identifies 11 modeling requirements for multidimensional data models. These requirements are derived from an assessment of complex data found in real-world applications. A survey of 14 multidimensional data models reveals shortcomings in meeting some of the requirements. Existing models do not support many-to-many relationships between facts and dimensions, lack built-in mechanisms for handling change and time, lack support for imprecision, and are generally unable to insert data with varying granularities. This paper defines an extended multidimensional data model and algebraic query language that address all 11 requirements. The model reuses the common multidimensional concepts of dimension hierarchies and granularities to capture imprecise data. For queries that cannot be answered precisely due to the imprecise data, techniques are proposed that take into account the imprecision in the grouping of the data, in the subsequent aggregate computation, and in the presentation of the imprecise result to the user. In addition, alternative queries unaffected by imprecision are offered. The data model and query evaluation techniques discussed in this paper can be implemented using relational database technology. The approach is also capable of exploiting multidimensional query processing techniques like pre-aggregation. This yields a practical solution with low computational overhead.
15090488
The Islands Approach to Nearest Neighbor Querying in Spatial Networks
Much research has recently been devoted to the data management foundations of location-based mobile services. In one important scenario, the service users are constrained to a transportation network. As a result, query processing in spatial road networks is of interest. We propose a versatile approach to k nearest neighbor computation in spatial networks, termed the Islands approach. By offering flexible yet simple means of balancing re-computation and pre-computation, this approach is able to manage the trade-off between query and update performance. The result is a single, efficient, and versatile approach to k nearest neighbor computation that obviates the need for using several k nearest neighbor approaches for supporting a single service scenario. The experimental comparison with the existing techniques uses real-world road network data and considers both I/O and CPU performance, for both queries and updates.
5224784
Nearest neighbor queries in road networks
With wireless communications and geo-positioning being widely available, it becomes possible to offer new e-services that provide mobile users with information about other mobile objects. This paper concerns active, ordered k-nearest neighbor queries for query and data objects that are moving in road networks. Such queries may be of use in many services.Specifically, we present an easily implementable data model that serves well as a foundation for such queries. We also present the design of a prototype system that implements the queries based on the data model. The algorithm used for the nearest neighbor search in the prototype is presented in detail. In addition, the paper reports on results from experiments with the prototype system.
6906174
Light-weight indexing of general bitemporal data
Most data managed by existing, real-world database applications is time referenced. Often, two temporal aspects of data are of interest, namely valid time, when data is true in the mini-world, and transaction time, when data is current in the database, resulting in so-called bitemporal data. Like spatial data, bitemporal data thus has associated two-dimensional regions. Such data is in part naturally now-relative: some data is true until the current time, and some data is part of the current database state. So, unlike for spatial data, bitemporal data regions may grow continuously. Existing indices, e.g., B/sup +/and R-trees, typically do not contend well with even small amounts of now-relative data. In contrast, the 4-R index presented in the paper is capable of indexing general bitemporal data efficiently. The different kinds of growing data regions are transformed into stationary regions, which are then indexed by R*-trees. Queries are also transformed to counter the data transformations, yielding a technique with perfect precision and recall. Performance studies indicate that the technique is competitive with the best existing index; and unlike this existing index, the new technique does not require extension of the DBMS kernel.