Tran Khanh Dang

Future Data and Security Engineering

The amount of unstructured data has grown exponentially during the past two decades and continues to grow at even faster rates. As a consequence, the efficient management of this kind of data came to play an important role in almost all organizations. Up to now, approaches from many different research fields, like information search and retrieval, text mining or query expansion and reformulation, have enabled us to extract and learn patterns in order to improve the management, retrieval and recommendation of documents. However, there are still many open questions, limitations and vulnerabilities that need to be addressed. This paper aims at identifying the current major challenges and research gaps in the field of “document enrichment, retrieval and recommendation”, introduces innovative ideas towards overcoming these limitations and weaknesses, and shows the benefits of adopting these ideas into real enterprise content management systems.

Two Novel Adaptive Symbolic Representations for Similarity Search in Time Series Databases

Since the last decade, we have seen an increasing level of interest in time series data mining due to its variety of real-world applications. Numerous representation models of time series have been proposed for data mining, including piecewise polynomial models, spectral models, and the recently proposed symbolic models, such as Symbolic Aggregate approXimation (SAX) and its multiresolution extension, indexable Symbolic Aggregate approXimation (iSAX). In spite of many advantages of dimensionality/numerosity reduction, and lower bounding distance measures, the quality of SAX approximation is highly dependent on the Gaussian distributed property of time series, especially in reduced-dimensionality literature. In this paper, we introduce a novel adaptive symbolic approach based on the combination of SAX and k¬-means algorithm which we call adaptive SAX (aSAX). The proposed representation greatly outperforms the classic SAX not only on the highly Gaussian distribution datasets, but also on the lack of Gaussian distribution datasets with a variety of dimensionality reduction. In addition to being competitive with, or superior to, the classic SAX, we extend aSAX to the multiresolution symbolic representation called indexable adaptive SAX (iaSAX). Our empirical experiments with real-world time series datasets confirm the theoretical analyses as well as the efficiency of the two proposed algorithms in terms of the tightness of lower bound, pruning power and number of random disk accesses.

The SH-tree: A Super Hybrid Index Structure for Multidimensional Data

Nowadays feature vector based similarity search is increasingly emerging in database systems. Consequently, many multidimensional data index techniques have been widely introduced to database researcher community. These index techniques are categorized into two main classes: SP (space partitioning)/KD-tree-based and DP (data partitioning)/R-tree-based. Recently, a hybrid index structure has been proposed. It combines both SP/KD-tree-based and DP/R-tree-based techniques to form a new, more efficient index structure. However, weaknesses are still existing in techniques above. In this paper, we introduce a novel and flexible index structure for multidimensional data, the SH-tree (Super Hybrid tree). Theoretical analyses show that the SH-tree is a good combination of both techniques with respect to both presentation and search algorithms. It overcomes the shortcomings and makes use of their positive aspects to facilitate efficient similarity searches.

Ensuring Correctness, Completeness, and Freshness for Outsourced Tree-Indexed Data

In an outsourced database service model, query assurance takes an important role among well-known security issues. To the best of our knowledge, however, none of the existing research work has dealt with ensuring the query assurance for outsourced tree-indexed data. To address this issue, the system must prove authenticity and data integrity, completeness, and freshness guarantees for the result set. These objectives imply that data in the result set is originated from the actual data owner and has not been tampered with; the server did not omit any tuples matching the query conditions; and the result set was generated with respect to the most recent snapshot of the database. In this paper, we propose a vanguard solution to provide query assurance for outsourced tree-indexed data on untrusted servers with high query assurance and at reasonable costs. Experimental results with real datasets confirm the effciency of our approach and theoretical analyses.

A Hilbert-based framework for preserving privacy in location-based services

Preserving users privacy has recently drawn special attention in the field of location-based services and many techniques such as k-anonymity or obfuscation have been suggested to protect users privacy. All of these traditional techniques are, however, geometry-based and separated from the database level. This separation causes the query processing to involve in two phases, querying the database to retrieve the exact locations of users and then modifying them to decrease the quality of this information. This two-phase process is time-consuming due to the number of disk accesses required to retrieve the users exact location. Also, these geometry-based techniques cannot guarantee location privacy when the adversary gains knowledge about the geography of the obfuscated region. We address these problems by proposing Hilbert-based framework for preserving users privacy and B

B ob -tree: an efficient B + -tree based index structure for geographic-aware obfuscation

The privacy protection of personal location information increasingly gains special attention in the field of location-based services, and obfuscation is the most popular technique aiming at protecting this sensitive information. However, all of the conventional obfuscation techniques are geometry-based and separated from the database level. Thus, the query processing has two timeconsuming phases due to the number of disk accesses required to retrieve the users exact location, and the location obfuscation. Also, since these techniques are geometry-based, they cannot assure location privacy when the adversary has knowledge about the geography of the obfuscated region. We address these problems by proposing Bob-tree, an index structure that is based on Bdual-tree and contains geographic-aware information on its nodes. Experiments show that Bob-tree provides a significant improvement over the algorithm separated from the database level for query processing time and location privacy protection.

X-STROWL: A generalized extension of XACML for context-aware spatio-temporal RBAC model with OWL

The rapid growth of location-based applications, geographic or large scale information systems has resulted in the demand of strictly controlling data access that requires specifying and enforcing fine grained policies with the variety of context-aware spatial and temporal restrictions. Besides, the interoperable use of distributed and heterogeneous data such as data sharing, data integration or data exchanging between different organizations has caused the formation and development of access control mechanisms using XML for enforcing security rules and policies in accordance with the international standards. In this paper, we propose an extension of XACML called the X-STROWL model for a generalized context-aware role-based access control (RBAC) model with the support of spatio-temporal restrictions and in conformity with the NIST standard for RBAC. In doing this, the XACML architecture is augmented with new functions and data types. In addition, policies are reorganized to adopt with the NIST standard. Besides, a set of conditions aimed to a certain circumstance can be generalized into a context profile and specified in the access control policies. The model also integrates the OWL ontology for semantic reasoning on hierarchical roles to simplify the specification of access control policies and increase the intelligence of the authorization decision engine.

STRoBAC: spatial temporal role based access control

The development of geography-based services and systems has created the demands in which access control is the primary concern for geospatial data security. Although there are a variety of models to manage geospatial data access, none of them can fulfil the access control requirements. The objective of this paper is to propose a model that can support both spatio-temporal aspects and other contextual conditions as well as access control based on the role of subject. We call this model Spatial Temporal Role Based Access Control (STRoBAC). In addition, we propose an extension of GeoXACML framework, which is highly scalable and can help in declaring and enforcing various types of rules, to support the proposed model. This is the crucial contribution of our research compared to the existing approaches and models.

An Adaptive Grid-Based Approach to Location Privacy Preservation

Location privacy protection is a key factor to the development of location-based services. Location privacy relates to the protection of a user’s identity, position, and path. In a grid-based approach, the user’s position is obfuscated in a number of cells. However, this grid does not allow users to adjust the cell size which relates to a minimum privacy level. Therefore, it is hard to fix various privacy requirements from different users. This paper proposes a flexible-grid-based approach as well as an algorithm to protect the user’s location privacy. However, the user can custom conveniently his grid due to his requirement of privacy. The overlap-area problem is also counted in the algorithm. By deeply investigating on our solution, we also discuss open research issues to make the solution feasible in the practice.

OST-Tree: An Access Method for Obfuscating Spatio-Temporal Data in Location Based Services

Since the development of location-based services, privacy-preserving has gained special attention and many algorithms aiming at protecting users privacy have been created such as obfuscation or k-anonymity. However, all of these researches separate the algorithms from the database level. Thus, the querying process has two phases, querying the database to retrieve the accurate positions of users and then modifying them to decrease the quality of location information. This two-phase process is time-consuming due to the number of disk accesses required to retrieve the users exact position. We address this problem by proposing OST-tree, a structure that embeds the users privacy policy in its node and obfuscates the spatio-temporal data. Experiments show that OST-tree provides an improvement over the algorithm separated from the database level for both querying costs and users privacy protection.