An Approach for Efficient and Secure Data Encryption Scheme for Spatial Data

The smart city system, which is a type of enterprise management system (EMS), automatically manages cities and schedules resources efficiently based on spatial data generated by devices, such as the Internet of Things and mobile. However, with the increasing deployment of technologies, including sensor and location-based services, their ever-growing spatial data are no longer managed efficiently by traditional EMS. To overcome this issue, we present SeFrame, which is a <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">s</u> patially <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e</u> nabled <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">frame</u> work for improving the efficiency of smart city EMS based on a distributed architecture. The framework supports a set of spatial queries, including: The range query, k-nearest neighbors query, and spatial join query. It benefits greatly from using the buffer-enabled partition method to eliminate duplicate results. In each partition, the local index based on combination of the quad-tree and grid index (CQG) significantly improves the spatial query efficiency in memory. CQG manages complex spatial objects, including a point, polygon, and polyline. By taking full advantage of the local index, SeFrame accesses skewed spatial data in constant time. In experiments, we demonstrated that the proposed method delivered superior performance in terms of scalability and query efficiency, in most cases.

Efficient spatial query processing in Graph Database Management Systems (GDBMSs) has become increasingly important owing to the prevalence of spatial graph data. However, current GDBMSs lack effective spatial indexing, causing performance issues with complex spatial graph queries. This study proposes a spatial index called Subgraph Integrated R-Tree (SGIR-Tree) for efficient spatial query processing in GDBMSs. The SGIR-Tree integrates the hierarchical R-Tree structure with the graph structure of GDBMSs by converting R-Tree elements into graph components like nodes and edges. The Minimum Bounding Rectangle (MBR) information of spatial objects and R-Tree nodes is stored as properties of these graph elements, and the leaf nodes are directly connected to the spatial nodes. This approach combines the efficiency of spatial indexing with the flexibility of graph databases, thereby allowing spatial query results to be directly utilized in graph traversal. Experiments using OpenStreetMap datasets demonstrate that the SGIR-Tree outperforms the previous approaches in terms of query overhead and index overhead. The results are expected to improve spatial graph data processing in various fields, including location-based service and urban planning, significantly advancing spatial data management in GDBMSs.

As the outsourcing of mobile sensitive data has been highlighted, data encryption schemes are required to protect the mobile sensitive data. Accordingly, it is necessary to develop not only a distributed index structure to efficiently manage the large amount of encrypted data, but also a query processing scheme over the encrypted data. To the best of our knowledge, there has been no research on index structure for the encrypted data. Meanwhile, the existing query processing schemes over the encrypted data can support limited types of queries. Also, without decrypting data, the existing schemes cannot support operations among data between different columns because they use different types of encryption schemes depending on their attribute types. To solve the problems, in this paper, we propose a bitmap-based distributed index structure and a query processing scheme for the encrypted data. The proposed distributed index structure guarantees data privacy preservation and performance improvement for the various types of queries. In addition, by processing a query over the encrypted data without data decryption, the proposed query processing scheme provide both high query performance and 100% accuracy while preserving the data privacy. Finally, we show from our performance analysis that our proposed index structure and query processing scheme are suitable for protecting the data privacy of the mobile sensitive data.

Driven by the cloud-first initiative taken by various governments and companies, it has become a common practice to outsource spatial data to cloud servers for a wide range of applications such as location-based services and geographic information systems. Searchable encryption is a common practice for outsourcing spatial data which enables search over encrypted data by sacrificing the full security via leaking some information about the queries to the server. However, these inherent leakages could equip the server to learn beyond what is considered in the scheme, in the worst-case allowing it to reconstruct of the database. Recently, a novel form of database reconstruction attack against such kind of outsourced spatial data was introduced (Markatou and Tamassia, IACR ePrint 2020/284), which is performed using common leakages of searchable encryption schemes, i.e., access and search pattern leakages. An access pattern leakage is utilized to achieve an order reconstruction attack, whereas both access and search pattern leakages are exploited for the full database reconstruction attack. In this paper, we propose two novel schemes for outsourcing encrypted spatial data supporting dynamic range search. Our proposed schemes leverage R+tree to partition the dataset and binary secret sharing to support secure range search. They further provide backward and content privacy and do not leak the access pattern, therefore being resilient against the above mentioned database reconstruction attacks. The evaluations and results on the real-world dataset demonstrate the practicality of our schemes, due to (a) the minimal round-trip between the client and server, and (b) the low computation and storage overhead on the client side.

Secure Location-Based Services (LBSs) in mobile cloud have gained widespread attention in the past decade. However, previous works mainly focus on spatial or spatial keyword query services and cannot support temporal filters simultaneously, which limits the service quality in practical applications. To address the above issue, we propose an efficient Keyword-associated Spatio-Temporal Structured Encryption (KSTSE) scheme that allows conjunctive queries according to spatio-temporal range and textual keywords on encrypted data in a mobile cloud. Specifically, we first transform geographic and temporal range queries into a unified encoding existence detection problem by combining S2 encoding and prefix encoding. Next, we build an efficient encrypted existence evaluation construction based on the circular shift and coalesce Bloom filter and symmetric hidden vector encryption. Finally, to support efficient queries on large-scale datasets, we design a hierarchical index tree structure, which can dynamically prune the search space according to the keyword and spatio-temporal range during the query process, reducing the query complexity to <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">O</i>(log<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">N</i>) Rigorous security analysis and performance evaluation show that the proposed KSTSE construction is adaptively secure under reasonable leakages and performs better than state-of-the-art schemes

Multiscale air quality modeling of the Northeastern United States

In location-based services (LBS), users have to continuously report their locations to the database server to entertain the service. For example, a user asking about her nearest gas station has to report her exact location to the database server. With untrustworthy servers, LBS may pose a major privacy threat on its users. In other words, the existing model of LBS trades service for user privacy. To tackle this privacy threat, several centralized privacy-preserving frameworks are proposed for LBS, in which a third trusted party is used as a middleware to blur user exact locations into spatial regions, in order to achieve k-anonymity, i.e., a user is indistinguishable among other k - 1 users. However, the centralized third trusted party could be the system bottleneck or single point of failure. The state-of-the-art peer-to-peer (P2P) communication technology adds a new dimension to the privacy-preserving techniques in LBS. The users holding P2P communication devices possess the ability to collaborate with one another to blur their exact locations into spatial regions without any help from centralized third trusted parties. In this paper, we present the challenges and research issues that emerge from deploying the P2P privacy-preserving framework in LBS.

A novel data encryption scheme using a set of exponential numbers is proposed in this paper which aims at achieving secure communications over public networks. The study begins with the definition of pattern of exponential number, where the decimal pattern of an exponential number ge is the result of taking e − 1 times multiplication the base element g by itself. The multiplication operation of two large integers involves the complicated carry conditions in each step, which is digit-dependence and can only be analyzed case by case. This implies that the pattern of a large exponential number is unpredictable. Next, we prove that nature number can be spanned by a set of exponential numbers, and there exists numerous spanning sets for one nature number, demonstrating that the expression of nature number using a set of exponential numbers is not unique. The unpredictable nature of exponential patterns and the abundant available sets of exponential numbers can contribute the concealment of information when data streams are encrypted by the exponential numbers. In addition, we prove that the base elements of a set of exponential numbers applied for spanning a nature number can be arbitrarily assigned. With this property, we can distribute a secret key sequence in advance as the base elements for data encryption, or this secret key can be shared using the Diffie-Hellman Key-Exchange system over public network. Finally, the encrypted message consists of a sequence of exponents and the index data, which indicates the relationship between the base elements and the secret key for decryption. The ciphertext is transmitted via the public channel to the receiver end; however, it can only be decrypted by those who own the secret key. Some examples are shown for demonstration the feasibility of the proposed encryption scheme.

The emerging new services for GPS and mobile users have developed applications that access and exploit spatial objects with new components (e.g. text and social network). Web objects, including blogs, tweets, photos and videos, are embedded into a map by the APIs of map service providers, where textual messages are associated with geographic information. Location-based social networking services, arising from Facebook and Foursquare, allow users to browse and share their traces of locations among the social networks. Among this class of applications, the highlight is that the retrieved spatial objects (e.g. points of interest and moving users) are featured with new components. Integrating such new components into the spatial query processing has produced large amounts of promising results. However, handling new components along with the retrieval of spatial objects increases the complexity of such joint query processing significantly. Thus, management over data from such multiple domains has been received considerable attention from database research community.\n\nIn this thesis, we introduce three interesting problems and study their sophisticated solutions for processing spatial objects with new components: (i) category-aware optimal route query (CORQ), (ii) social and spatial ranking query (SSRQ), and (iii) efficient notification of meeting point (ENMP) query. Our results for (i) and (ii) facilitate the retrieval of spatial objects from multiple domains while our solutions for (iii) provide effective tools for synchronous management of multiple moving users from a social network. Category-aware optimal route queries (CORQ) are generalized from the traveling salesman problem and enable users to retrieve shortest routes covering selected categories. Social and spatial ranking queries (SSRQ) are relevant to spatial object recommendations using social information and allow users to obtain the spatial objects that not only are near their locations but also impress them with high social influence. Efficient notification of meeting point (ENMP) queries are variants of aggregate nearest neighbor queries and provide real-time rearrangement for multiple moving users according to their locations.\n\nQuery processing in such multiple domains is complicated due to the mixture of domain information and their integration within one search. Naïve algorithms for these problems incur either numerous expensive evaluations or massive communication cost, which render them inapplicable to large datasets. Our main research purpose is to design efficient and effective solutions for the proposed problems, that avoid the aforementioned shortcomings of naïve algorithms.

Location-based services (LBSs) have entered into people’s daily lives with the evolution of telecommunication technologies. LBSs are the services that consider the locations of users to provide tailored information. We study a variety of location-based queries in urban environments. Mainly, we focus on processing such queries in the indoor space motivated by the abundance of applications of indoor LBSs and lack of existing studies supporting indoor spatial queries. Although the spatial query processing has been extensively studied for outdoor space, these techniques are not efficient when applied to indoor venues due to inherent differences in the topology of indoor space.

With the pervasiveness of location-aware mobile electronic devices and the advances of wireless communications, location-based services (LBS), which can help people enjoy a convenient life, has attracted considerable interest recently. However, the privacy issues of LBS are still challenging today. Aiming at the challenges, in this paper, we present a new efficient and privacy-preserving LBS query scheme in outsourced cloud, i.e., EPQ, for pervasive smartphones. In the EPQ scheme, the LBS provider's data are first outsourced to the cloud server in an encrypted manner, and then, a registered user can get accurate LBS query results without divulging his/her location information to the LBS provider and the cloud server. Specifically, based on an improved homomorphic encryption technique over a composite order group, a special spatial range query algorithm SRQC over ciphertext is proposed, with which EPQ achieves privacy preservation of user's query and confidentiality of LBS data in the outsourced cloud server. Through detailed security analysis, we show that EPQ can resist various known security threats. In addition, we also implement EPQ over a smartphone and three workstations with a real LBS data set, and extensive simulation results further demonstrate that the proposed EPQ scheme is highly efficient at the smartphone side and can be implemented effectively in the cloud server.

Modern location-based applications rely extensively on the efficient processing of spatial data and queries. Spatial query engines are commonly engineered as an extension to a relational database or a cluster-computing framework. Large parts of the spatial processing runtime is spent on evaluating spatial predicates and traversing spatial indexing structures. Typical high-level implementations of these spatial structures incur significant interpretive overhead, which increases latency and lowers throughput. A promising idea to improve the performance of spatial workloads is to leverage native code generation techniques that have become popular in relational query engines. However, architecting a spatial query compiler is challenging since spatial processing has fundamentally different execution characteristics from relational workloads in terms of data dimensionality, indexing structures, and predicate evaluation. In this paper, we discuss the underlying reasons why standard query compilation techniques are not fully effective when applied to spatial workloads, and we demonstrate how a particular style of query compilation based on techniques borrowed from partial evaluation and generative programming manages to avoid most of these difficulties by extending the scope of custom code generation into the data structures layer. We extend the LB2 main-memory query compiler, a relational engine developed in this style, with spatial data types, predicates, indexing structures, and operators. We show that the spatial extension matches the performance of specialized library code and outperforms relational and map-reduce extensions.

With the rapid development of Location-Based Services (LBS), a large number of LBS providers outsource spatial data to cloud servers to reduce their high computational and storage burdens, but meanwhile incur some security issues such as location privacy leakage. Thus, extensive privacy-preserving LBS schemes have been proposed. However, the existing solutions using Bloom filter do not take into account the redundant bits that do not map information in Bloom filter, resulting in high computational overheads, and reveal the inclusion relationship in Bloom filter. To solve these issues, we propose an efficient Privacy-preserving Spatial Range Query (PSRQ) scheme by skillfully combining Geohash algorithm with Circular Shift and Coalesce Bloom Filter (CSC-BF) framework and Symmetric-key Hidden Vector Encryption (SHVE), which not only greatly reduces the computational cost of generating token but also speeds up the query efficiency on large-scale datasets. In addition, we design a Confused Bloom Filter (CBF) to confuse the inclusion relationship by confusing the values of 0 and 1 in the Bloom filter. Base on this, we further propose a more secure and practical enhanced scheme PSRQ <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">+</sup> by using CBF and Geohash algorithm, which can support more query ranges and achieve adaptive security. Finally, formal security analysis proves that our schemes are secure against Indistinguishability under Chosen-Plaintext Attacks (IND-CPA) and PSRQ <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">+</sup> achieves adaptive IND-CPA, and extensive experimental tests demonstrate that our schemes using million-level dataset improve the query efficiency by 100x compared with previous state-of-the-art solutions.

With the development of Scalable Vector Graphics (SVG) used in Location Based Services (LBS), the presentation and organization of spatial data based SVG became a burning question. The paper extracted the key technologies about presentation and organization of SVG_based spatial data according to the trend of development. The main points concluded acquisition of SVG data, presentation of attributes, construction of spatial data, and compression of SVG data etc. The paper also pointed out some current problems and research orientations in the future in some areas which concluded transform from multiple heterogeneous data to SVG data, presentation and construction of SVG_based data, and data compression etc. The paper can serve as a guide to used SVG in LBS.

With the increasing pervasiveness of the geo-positioning technologies, there is an enormous amount of spatio-textual objects available in many applications such as location based services and social networks. Consequently, various types of spatial keyword searches which explore both locations and textual descriptions of the objects have been intensively studied by the research communities and commercial organizations. In many important applications (e.g., location based services), the closeness of two spatial objects is measured by the road network distance. Moreover, the result diversification is becoming a common practice to enhance the quality of the search results. Motived by the above facts, in this paper we study the problem of diversified spatial keyword search on road networks which considers both the relevance and the spatial diversity of the results. An efficient signature-based inverted indexing technique is proposed to facilitate the spatial keyword query processing on road networks. Then we develop an efficient diversified spatial keyword search algorithm by taking advantage of spatial keyword pruning and diversity pruning techniques. Comprehensive experiments on real and synthetic data clearly demonstrate the efficiency of our methods.