The Experts below are selected from a list of 323472 Experts worldwide ranked by ideXlab platform
Haitao Wang - One of the best experts on this subject based on the ideXlab platform.
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SecureComm (1) - Tree-Based Multi-Dimensional Range Search on Encrypted Data with Enhanced Privacy
Lecture Notes of the Institute for Computer Sciences Social Informatics and Telecommunications Engineering, 2015Co-Authors: Boyang Wang, Yantian Hou, Haitao WangAbstract:With searchable encryption, a data user is able to perform meaningful search on encrypted data stored in the public cloud without revealing data privacy. Besides handling simple queries (e.g., keyword queries), complex search functions, such as multi-Dimensional (conjunctive) Range queries, have also been studied in several approaches to provide search functionalities over multi-Dimensional data. However, current works supporting multi-Dimensional Range queries either only achieve linear search complexity or reveal additional private information to the public cloud. In this paper, we propose a tree-based symmetric-key searchable encryption to support multi-Dimensional Range queries on encrypted data. Besides protecting data privacy, our proposed scheme is able to achieve faster-than-linear search, query privacy and single-Dimensional privacy simultaneously compared to previous solutions. More specifically, we formally define the security of our proposed scheme, prove that it is selectively secure, and demonstrate its faster-than-linear efficiency with experiments over a real-world dataset.
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maple scalable multi Dimensional Range search over encrypted cloud data with tree based index
Computer and Communications Security, 2014Co-Authors: Boyang Wang, Yantian Hou, Haitao WangAbstract:Cloud computing promises users massive scale outsourced data storage services with much lower costs than traditional methods. However, privacy concerns compel sensitive data to be stored on the cloud server in an encrypted form. This posts a great challenge for effectively utilizing cloud data, such as executing common SQL queries. A variety of searchable encryption techniques have been proposed to solve this issue; yet efficiency and scalability are still the two main obstacles for their adoptions in real-world datasets, which are multi-Dimensional in general. In this paper, we propose a tree-based public-key Multi-Dimensional Range Searchable Encryption (MDRSE) to overcome the above limitations. Specifically, we first formally define the leakage function and security of a tree-based MDRSE. Then, by leveraging an existing predicate encryption in a novel way, our tree-based MDRSE efficiently indexes and searches over encrypted cloud data with multi-Dimensional tree structures (i.e., R-trees). Moreover, our scheme is able to protect single-Dimensional privacy while previous efficient solutions fail to achieve. Our scheme is selectively secure, and through extensive experimental evaluation on a large-scale real-world dataset, we show the efficiency and scalability of our scheme.
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AsiaCCS - Maple: scalable multi-Dimensional Range search over encrypted cloud data with tree-based index
Proceedings of the 9th ACM symposium on Information computer and communications security, 2014Co-Authors: Boyang Wang, Yantian Hou, Haitao WangAbstract:Cloud computing promises users massive scale outsourced data storage services with much lower costs than traditional methods. However, privacy concerns compel sensitive data to be stored on the cloud server in an encrypted form. This posts a great challenge for effectively utilizing cloud data, such as executing common SQL queries. A variety of searchable encryption techniques have been proposed to solve this issue; yet efficiency and scalability are still the two main obstacles for their adoptions in real-world datasets, which are multi-Dimensional in general. In this paper, we propose a tree-based public-key Multi-Dimensional Range Searchable Encryption (MDRSE) to overcome the above limitations. Specifically, we first formally define the leakage function and security of a tree-based MDRSE. Then, by leveraging an existing predicate encryption in a novel way, our tree-based MDRSE efficiently indexes and searches over encrypted cloud data with multi-Dimensional tree structures (i.e., R-trees). Moreover, our scheme is able to protect single-Dimensional privacy while previous efficient solutions fail to achieve. Our scheme is selectively secure, and through extensive experimental evaluation on a large-scale real-world dataset, we show the efficiency and scalability of our scheme.
Pooya Davoodi - One of the best experts on this subject based on the ideXlab platform.
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ESA - The Encoding Complexity of Two Dimensional Range Minimum Data Structures
Lecture Notes in Computer Science, 2013Co-Authors: Gerth Stølting Brodal, Andrej Brodnik, Pooya DavoodiAbstract:In the two-Dimensional Range minimum query problem an input matrix A of dimension m ×n, m ≤ n, has to be preprocessed into a data structure such that given a query rectangle within the matrix, the position of a minimum element within the query Range can be reported. We consider the space complexity of the encoding variant of the problem where queries have access to the constructed data structure but can not access the input matrix A, i.e. all information must be encoded in the data structure. Previously it was known how to solve the problem with space O(mn min {m,logn}) bits (and with constant query time), but the best lower bound was Ω(mnlogm) bits, i.e. leaving a gap between the upper and lower bounds for non-quadratic matrices. We show that this space lower bound is optimal by presenting an encoding scheme using O(mnlogm) bits. We do not consider query time.
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ESA - Two Dimensional Range minimum queries and fibonacci lattices
Algorithms – ESA 2012, 2012Co-Authors: Gerth Stølting Brodal, Pooya Davoodi, Moshe Lewenstein, Rajeev Raman, S. Srinivasa RaoAbstract:Given a matrix of size N, two Dimensional Range minimum queries (2D-RMQs) ask for the position of the minimum element in a rectangular Range within the matrix. We study trade-offs between the query time and the additional space used by indexing data structures that support 2D-RMQs. Using a novel technique--the discrepancy properties of Fibonacci lattices--we give an indexing data structure for 2D-RMQs that uses O(N/c) bits additional space with O(clogc(loglogc)2) query time, for any parameter c, 4≤c≤N. Also, when the entries of the input matrix are from {0,1}, we show that the query time can be improved to O(clogc) with the same space usage.
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LATIN - Two-Dimensional Range diameter queries
LATIN 2012: Theoretical Informatics, 2012Co-Authors: Pooya Davoodi, Michiel Smid, Freek Van WalderveenAbstract:Given a set of n points in the plane, Range diameter queries ask for the furthest pair of points in a given axis-parallel rectangular Range. We provide evidence for the hardness of designing space-efficient data structures that support Range diameter queries by giving a reduction from the set intersection problem. The difficulty of the latter problem is widely acknowledged and is conjectured to require nearly quadratic space in order to obtain constant query time, which is matched by known data structures for both problems, up to polylogarithmic factors. We strengthen the evidence by giving a lower bound for an important subproblem arising in solutions to the Range diameter problem: computing the diameter of two convex polygons, that are separated by a vertical line and are preprocessed independently, requires almost linear time in the number of vertices of the smaller polygon, no matter how much space is used. We also show that Range diameter queries can be answered much more efficiently for the case of points in convex position by describing a data structure of size O(nlogn) that supports queries in O(logn) time.
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On Space Efficient Two Dimensional Range Minimum Data Structures
Algorithmica, 2011Co-Authors: Gerth Stølting Brodal, Pooya Davoodi, S. Srinivasa RaoAbstract:The two Dimensional Range minimum query problem is to preprocess a static m by n matrix (two Dimensional array) A of size N=m⋅n, such that subsequent queries, asking for the position of the minimum element in a rectangular Range within A, can be answered efficiently. We study the trade-off between the space and query time of the problem. We show that every algorithm enabled to access A during the query and using a data structure of size O(N/c) bits requires Ω(c) query time, for any c where 1≤c≤N. This lower bound holds for arrays of any dimension. In particular, for the one Dimensional version of the problem, the lower bound is tight up to a constant factor. In two dimensions, we complement the lower bound with an indexing data structure of size O(N/c) bits which can be preprocessed in O(N) time to support O(clog 2 c) query time. For c=O(1), this is the first O(1) query time algorithm using a data structure of optimal size O(N) bits. For the case where queries can not probe A, we give a data structure of size O(N⋅min {m,log n}) bits with O(1) query time, assuming m≤n. This leaves a gap to the space lower bound of Ω(Nlog m) bits for this version of the problem.
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ESA (2) - On space efficient two Dimensional Range minimum data structures
Algorithms – ESA 2010, 2010Co-Authors: Gerth Stølting Brodal, Pooya Davoodi, S. Srinivasa RaoAbstract:The two Dimensional Range minimum query problem is to preprocess a static two Dimensional m by n array A of size N = m ċ n, such that subsequent queries, asking for the position of the minimum element in a rectangular Range within A, can be answered efficiently. We study the trade-off between the space and query time of the problem. We show that every algorithm enabled to access A during the query and using O(N/c) bits additional space requires Ω(c) query time, for any c where 1 ≤ c ≤ N. This lower bound holds for any dimension. In particular, for the one Dimensional version of the problem, the lower bound is tight up to a constant factor. In two dimensions, we complement the lower bound with an indexing data structure of size O(N/c) bits additional space which can be preprocessed in O(N) time and achieves O(c log2 c) query time. For c = O(1), this is the first O(1) query time algorithm using optimal O(N) bits additional space. For the case where queries can not probe A, we give a data structure of size O(N ċ min{m, log n}) bits with O(1) query time, assuming m ≤ n. This leaves a gap to the lower bound of Ω(N log m) bits for this version of the problem.
Gerth Stølting Brodal - One of the best experts on this subject based on the ideXlab platform.
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ESA - The Encoding Complexity of Two Dimensional Range Minimum Data Structures
Lecture Notes in Computer Science, 2013Co-Authors: Gerth Stølting Brodal, Andrej Brodnik, Pooya DavoodiAbstract:In the two-Dimensional Range minimum query problem an input matrix A of dimension m ×n, m ≤ n, has to be preprocessed into a data structure such that given a query rectangle within the matrix, the position of a minimum element within the query Range can be reported. We consider the space complexity of the encoding variant of the problem where queries have access to the constructed data structure but can not access the input matrix A, i.e. all information must be encoded in the data structure. Previously it was known how to solve the problem with space O(mn min {m,logn}) bits (and with constant query time), but the best lower bound was Ω(mnlogm) bits, i.e. leaving a gap between the upper and lower bounds for non-quadratic matrices. We show that this space lower bound is optimal by presenting an encoding scheme using O(mnlogm) bits. We do not consider query time.
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ESA - Two Dimensional Range minimum queries and fibonacci lattices
Algorithms – ESA 2012, 2012Co-Authors: Gerth Stølting Brodal, Pooya Davoodi, Moshe Lewenstein, Rajeev Raman, S. Srinivasa RaoAbstract:Given a matrix of size N, two Dimensional Range minimum queries (2D-RMQs) ask for the position of the minimum element in a rectangular Range within the matrix. We study trade-offs between the query time and the additional space used by indexing data structures that support 2D-RMQs. Using a novel technique--the discrepancy properties of Fibonacci lattices--we give an indexing data structure for 2D-RMQs that uses O(N/c) bits additional space with O(clogc(loglogc)2) query time, for any parameter c, 4≤c≤N. Also, when the entries of the input matrix are from {0,1}, we show that the query time can be improved to O(clogc) with the same space usage.
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On Space Efficient Two Dimensional Range Minimum Data Structures
Algorithmica, 2011Co-Authors: Gerth Stølting Brodal, Pooya Davoodi, S. Srinivasa RaoAbstract:The two Dimensional Range minimum query problem is to preprocess a static m by n matrix (two Dimensional array) A of size N=m⋅n, such that subsequent queries, asking for the position of the minimum element in a rectangular Range within A, can be answered efficiently. We study the trade-off between the space and query time of the problem. We show that every algorithm enabled to access A during the query and using a data structure of size O(N/c) bits requires Ω(c) query time, for any c where 1≤c≤N. This lower bound holds for arrays of any dimension. In particular, for the one Dimensional version of the problem, the lower bound is tight up to a constant factor. In two dimensions, we complement the lower bound with an indexing data structure of size O(N/c) bits which can be preprocessed in O(N) time to support O(clog 2 c) query time. For c=O(1), this is the first O(1) query time algorithm using a data structure of optimal size O(N) bits. For the case where queries can not probe A, we give a data structure of size O(N⋅min {m,log n}) bits with O(1) query time, assuming m≤n. This leaves a gap to the space lower bound of Ω(Nlog m) bits for this version of the problem.
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ESA (2) - On space efficient two Dimensional Range minimum data structures
Algorithms – ESA 2010, 2010Co-Authors: Gerth Stølting Brodal, Pooya Davoodi, S. Srinivasa RaoAbstract:The two Dimensional Range minimum query problem is to preprocess a static two Dimensional m by n array A of size N = m ċ n, such that subsequent queries, asking for the position of the minimum element in a rectangular Range within A, can be answered efficiently. We study the trade-off between the space and query time of the problem. We show that every algorithm enabled to access A during the query and using O(N/c) bits additional space requires Ω(c) query time, for any c where 1 ≤ c ≤ N. This lower bound holds for any dimension. In particular, for the one Dimensional version of the problem, the lower bound is tight up to a constant factor. In two dimensions, we complement the lower bound with an indexing data structure of size O(N/c) bits additional space which can be preprocessed in O(N) time and achieves O(c log2 c) query time. For c = O(1), this is the first O(1) query time algorithm using optimal O(N) bits additional space. For the case where queries can not probe A, we give a data structure of size O(N ċ min{m, log n}) bits with O(1) query time, assuming m ≤ n. This leaves a gap to the lower bound of Ω(N log m) bits for this version of the problem.
Boyang Wang - One of the best experts on this subject based on the ideXlab platform.
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SecureComm (1) - Tree-Based Multi-Dimensional Range Search on Encrypted Data with Enhanced Privacy
Lecture Notes of the Institute for Computer Sciences Social Informatics and Telecommunications Engineering, 2015Co-Authors: Boyang Wang, Yantian Hou, Haitao WangAbstract:With searchable encryption, a data user is able to perform meaningful search on encrypted data stored in the public cloud without revealing data privacy. Besides handling simple queries (e.g., keyword queries), complex search functions, such as multi-Dimensional (conjunctive) Range queries, have also been studied in several approaches to provide search functionalities over multi-Dimensional data. However, current works supporting multi-Dimensional Range queries either only achieve linear search complexity or reveal additional private information to the public cloud. In this paper, we propose a tree-based symmetric-key searchable encryption to support multi-Dimensional Range queries on encrypted data. Besides protecting data privacy, our proposed scheme is able to achieve faster-than-linear search, query privacy and single-Dimensional privacy simultaneously compared to previous solutions. More specifically, we formally define the security of our proposed scheme, prove that it is selectively secure, and demonstrate its faster-than-linear efficiency with experiments over a real-world dataset.
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maple scalable multi Dimensional Range search over encrypted cloud data with tree based index
Computer and Communications Security, 2014Co-Authors: Boyang Wang, Yantian Hou, Haitao WangAbstract:Cloud computing promises users massive scale outsourced data storage services with much lower costs than traditional methods. However, privacy concerns compel sensitive data to be stored on the cloud server in an encrypted form. This posts a great challenge for effectively utilizing cloud data, such as executing common SQL queries. A variety of searchable encryption techniques have been proposed to solve this issue; yet efficiency and scalability are still the two main obstacles for their adoptions in real-world datasets, which are multi-Dimensional in general. In this paper, we propose a tree-based public-key Multi-Dimensional Range Searchable Encryption (MDRSE) to overcome the above limitations. Specifically, we first formally define the leakage function and security of a tree-based MDRSE. Then, by leveraging an existing predicate encryption in a novel way, our tree-based MDRSE efficiently indexes and searches over encrypted cloud data with multi-Dimensional tree structures (i.e., R-trees). Moreover, our scheme is able to protect single-Dimensional privacy while previous efficient solutions fail to achieve. Our scheme is selectively secure, and through extensive experimental evaluation on a large-scale real-world dataset, we show the efficiency and scalability of our scheme.
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AsiaCCS - Maple: scalable multi-Dimensional Range search over encrypted cloud data with tree-based index
Proceedings of the 9th ACM symposium on Information computer and communications security, 2014Co-Authors: Boyang Wang, Yantian Hou, Haitao WangAbstract:Cloud computing promises users massive scale outsourced data storage services with much lower costs than traditional methods. However, privacy concerns compel sensitive data to be stored on the cloud server in an encrypted form. This posts a great challenge for effectively utilizing cloud data, such as executing common SQL queries. A variety of searchable encryption techniques have been proposed to solve this issue; yet efficiency and scalability are still the two main obstacles for their adoptions in real-world datasets, which are multi-Dimensional in general. In this paper, we propose a tree-based public-key Multi-Dimensional Range Searchable Encryption (MDRSE) to overcome the above limitations. Specifically, we first formally define the leakage function and security of a tree-based MDRSE. Then, by leveraging an existing predicate encryption in a novel way, our tree-based MDRSE efficiently indexes and searches over encrypted cloud data with multi-Dimensional tree structures (i.e., R-trees). Moreover, our scheme is able to protect single-Dimensional privacy while previous efficient solutions fail to achieve. Our scheme is selectively secure, and through extensive experimental evaluation on a large-scale real-world dataset, we show the efficiency and scalability of our scheme.
Azzedine Boukerche - One of the best experts on this subject based on the ideXlab platform.
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HD Tree: A novel data structure to support multi-Dimensional Range query for P2P networks
Journal of Parallel and Distributed Computing, 2011Co-Authors: Azzedine BoukercheAbstract:There are two basic concerns for supporting multi-Dimensional Range query in P2P overlay networks. The first is to preserve data locality in the process of data space partitioning, and the second is the maintenance of data locality among data Ranges with an exponentially expanding and extending rate. The first problem has been well addressed by using recursive decomposition schemes, such as Quad-tree, K-d tree, Z-order, and Hilbert curve. On the other hand, the second problem has been recently identified by our novel data structure: HD Tree. In this paper, we explore how data locality can be easily maintained, and how Range query can be efficiently supported in HD Tree. This is done by introducing two basic routing strategies: hierarchical routing and distributed routing. Although hierarchical routing can be applied to any two nodes in the P2P system, it generates high volume traffic toward nodes near the root, and has very limited options to cope with node failure. On the other hand, distributed routing concerns source and destination pairs only at the same depth, but traffic load is bound to some nodes at two neighboring depths, and multiple options can be found to redirect a routing request. Because HD Tree supports multiple routes between any two nodes in the P2P system, routing in HD Tree is very flexible; it can be designed for many purposes, like fault tolerance, or dynamic load balancing. Distributed routing oriented combined routing (DROCR) algorithm is one such routing strategy implemented so far. It is a hybrid algorithm combining advantages from both hierarchical routing and distributed routing. The experimental results show that DROCR algorithm achieves considerable performance gain over the equivalent tree routing at the highest depth examined. For supporting multi-Dimensional Range query, the experimental results indicate that the exponentially expanding and extending rate have been effectively controlled and minimized by HD Tree overlay structure and DROCR routing.
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Error-Resilient Routing for Supporting Multi-Dimensional Range Query in HD Tree
2011 IEEE ACM 15th International Symposium on Distributed Simulation and Real Time Applications, 2011Co-Authors: Yunfeng Gu, Azzedine BoukercheAbstract:The Hierarchically Distributed Tree (HD Tree) is a novel distributed data structure built over a complete tree. The purpose of proposing this new data structure is to better support multi-Dimensional Range query in the distributed environment. HD Tree doubles the number of neighbors at the cost of doubling total links of a tree. The routing operation in HD Tree is supposed to be highly error-resilient. In HD Tree, the routing table size is determined by the system parameter k, and the performance of all basic operations are bound by O(lg(n)). Multiple routing options can be found between any two nodes in the system. This paper explores fault tolerant routing strategies in HD Tree. The experimental results produce very limited and unnoticeable increases in routing cost when conducting Range queries in an error-prone routing environment. The maximum failures we have tested are about 5 percent of routing nodes. The experimental results also indicate that higher fault tolerant capability requires finer consideration in the design of the error-resilient routing strategy.
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DS-RT - Supporting Multi-Dimensional Range Query in HD Tree
2010 IEEE ACM 14th International Symposium on Distributed Simulation and Real Time Applications, 2010Co-Authors: Azzedine Boukerche, Regina B. AraujoAbstract:There are two basic concerns for supporting multi-Dimensional Range query in P2P overlay networks. The first is to preserve data locality in the process of data space partitioning, and the second is the maintenance of data locality among data items with an exponentially expanding rate and an exponentially extending rate. The first problem has been well addressed by using recursive decomposition schemes, such as Quad tree, K-d tree, Z-order, and Hilbert curve. While the second problem was recently identified by our novel data structure: HD Tree. This paper is a follow-up to our previous work in HD Tree. In this paper, we explore how data locality can be easily maintained, and how Range query can be efficiently supported in HD Tree. This is done by introducing two basic routing strategies, hierarchical routing and distributed routing. Although hierarchical routing can be applied to any two nodes in the system, it generates high volume traffic towards nodes near the root, and has very limited options to cope with a node failure. On the other hand, distributed routing concerns source and destination pairs only at the same depth, but traffic load is bound to some nodes at two neighboring depths, and multiple options can be found to redirect a routing request. Because HD Tree supports multiple routes between any two nodes in the system, routing in HD Tree is very flexible, and can be designed for many purposes, like fault tolerance, or dynamic load balancing. Distributed Routing Oriented Combined Routing algorithm is one such routing strategies implemented so far. It is a hybrid algorithm combining advantages from both the hierarchical routing and the distributed routing. The experimental results show that the DROCR algorithm achieves considerable performance gain over the equivalent tree routing at the highest depth examined. In the experiment of supporting multi-Dimensional Range query, we employ the Z-order space filling curve over the HD Tree overlay layer. We are expecting that the performance of Range query will vary proportionally with the change of Range size, and reasonably with the increase of Dimensionality.