Verification Objects Research Articles

An extension of atomic mean square displacement method for calculating melting temperatures in II-VI compounds

Understanding how solids melt and determining their melting temperatures is of great significance for studying the properties of materials. Based on the main idea of Lindemann's melting criterion and the first-principles calculation of density functional theory, we proposed the atomic mean square displacement method to predict the melting temperature of the material. In this paper, the application range of this method for calculating melting temperature is extended. 8 kinds of Ⅱ-Ⅵ compounds were selected as verification objects. The results show the accuracy of our method in predicting the melting temperature of Ⅱ-Ⅵ compounds.

A Case Study on the Effect of Atmospheric Density Calibration on Orbit Predictions with Sparse Angular Data

Accurately modeling the density of atmospheric mass is critical for orbit determination and prediction of space objects. Existing atmospheric mass density models (ADMs) have an accuracy of about 15%. Developing high-precision ADMs is a long-term goal that requires a better understanding of atmospheric density characteristics, more accurate modeling methods, and improved spatiotemporal data. This study proposes a method for calibrating ADMs using sparse angular data of space objects in low-Earth orbit over a certain period of time. Applying the corrected ADM not only improves the accuracy of orbit determination, but also enhances the accuracy of orbit prediction beyond the correction period. The study compares the impact of two calibration methods: atmospheric mass density model coefficient (ADMC) calibration and high precision satellite drag model (HASDM) calibration on the accuracy of orbit prediction of space objects. One month of ground-based telescope array angular data is used to validate the results. Space objects are classified as calibration objects, participating in ADM calibration, and verification objects, inside and outside the calibration orbit region, respectively. The results show that applying the calibrated ADM can significantly increase the accuracy of orbit prediction. For objects within the calibration orbit region, the calibration object’s orbit prediction error was reduced by about 55%, while that of verification objects was reduced by about 45%. The reduction in orbit prediction error outside this region was about 30%. This proposed method contributes significantly to the development of more reliable ADMs for orbit prediction of space objects with sparse angular data and can provide significant academic value in the field of space situational awareness.

A Security-Enhanced Query Result Verification Scheme for Outsourced Data in Location-Based Services

Location-based services (LBSs) facilitate people’s lives; location-based service providers (LBSPs) usually outsource services to third parties to provide better services. However, the third party is a dishonest entity that might return incorrect or incomplete query results under the consideration of saving storage space and computation resources. In this paper, we propose a security-enhanced query result verification scheme (SEQRVS) for the outsourced data in a LBS. Specifically, while retaining fine-grained query result verification, we improve the construction process of verification objects to enhance the security of the outsourced data. To prevent the third party from deducing the knowledge of the outsourced data stored in itself (statistically), our scheme designs a novel storage structure to enhance the ability of privacy preservation for the outsourced data. Furthermore, based on the secure keyword search and query result verification mode proposed in our scheme, the user cannot only verify the correctness and completeness of the query result but also achieve consistency verification by the blockchain. Finally, the security analysis and extensive simulation results show the security and practicality of the proposed scheme.

Efficient secure and verifiable location-based skyline queries over encrypted data

Supporting secure location-based services on encrypted data that is outsourced to cloud computing platforms remains an ongoing challenge for efficiency due to expensive ciphertext calculation overhead. Furthermore, since the clouds may not be trustworthy or even malicious, data security and result authenticity has caused huge concerns. Unfortunately, little work can enable query efficiency, dataset confidentiality and result authenticity to be commendably guaranteed. In this paper, we demonstrate the potential of supporting <u>s</u>ecure and <u>v</u>erifiable <u>l</u>ocation-based <u>s</u>kyline <u>q</u>ueries (SVLSQ). First, we devise a novel and unified structure, named semi-blind R-tree (SR-tree), which protects the query unlinkability. Based on SR-tree, we propose an authenticated data structure, named secure and verifiable scope R-tree (SVSR-tree). Then, we develop several secure protocols based on SVSR-tree to accelerate the query efficiency and reduce the size of verification objects. Our method avoids compromising the privacy of datasets, queries, results and access patterns. Meanwhile, it authenticates the soundness and completeness of the skyline results while preserving privacy. Finally, we analyze the complexity and security of SVLSQ. Findings from the performance evaluation illustrate that SVLSQ is a dramatically efficient method in terms of query (no less than 3 orders of magnitude faster than other solutions) and verification.

Authoritative and Unbiased Responses to Geographic Queries

A protocol for processing geographic data is proposed to guarantee authoritative and unbiased responses to geographic queries, without the need to rely on trusted third parties. The integrity of the proposed authoritative and unbiased geographic services (AUGS) protocol is guaranteed by employing novel hash tree based authenticated data structures (ADS) in conjunction with a blockchain ledger. Hash tree based ADSes are used to incrementally compute a succinct dynamic commitments to AUGS data. A blockchain ledger is used to record 1) transactions that trigger updates to AUGS data, and 2) the updated cryptographic commitments to AUGS data. Untrusted service providers are required to provide verification objects (VOs) as proof-of-correctness of their responses to AUGS queries. Anyone with access to commitments in ledger entries can verify the proof.

Research on a Design and Manufacturing Integration Mode Based on a Full 3D Model

In view of issues such as low reuse rate of three-dimensional design model in process and production during product research and design, insufficient ability of 3D process design and optimization application, and urgent needs for a closed-loop design-manufacture-inspection integrated control system based on 3D model, this paper is centered around the digital definition and application technologies based on 3D models and facing full process information of product design-process design-production-inspection, combining the product development process, and studies the design and manufacturing integration framework based on a full 3D model, software architecture and data integration management architecture. This paper too selects products with multi-subject features as verification objects and verifies the feasibility and superiority of the integrated design-production-inspection development mode based on a full 3D model.

Comment on “Achieving Secure, Universal, and Fine-Grained Query Results Verification for Secure Search Scheme Over Encrypted Cloud Data”

Recently in IEEE Transactions on Cloud Computing (TCC), Yin <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">et al.</i> <xref ref-type="bibr" rid="ref5" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[5]</xref> designed a fine-grained query verification mechanism where a novel certificateless short signature scheme is proposed for validating the data of encrypted query results. Despite the authors alleged that their scheme achieves the existential unforgeability to ensure the authenticity of verification objects, we found that this scheme fails to resist the forgery attack. Specifically, through launching the concrete attacks, a malicious adversary can forge a signature on any verification object without being detected.

Topic-based rank search with verifiable social data outsourcing

As the explosive development of social network sites, social data has successfully captured either individuals’ or entities’ attention due to having tremendous commercial value. The initial step to my various insights from social data is how to obtain authentic social data. Social data outsourcing as a new paradigm has been pervasive, in which a social data provider collects integrity social data from different social network sites and resells to data consumers on demand. However, some dishonest activities, like adding fake data, deleting/modifying raw data, drive us to consider verifiable topic-based rank search problem on social data outsourcing scenario. To guarantee the authenticity of social data, we propose two schemes. In our basic scheme, social network sites generate unforgeable auxiliary information and outsource to the social data provider as well as social data. Data consumers can probabilistically verify the correctness and completeness of the query results with the help of verification objects derived from auxiliary information. To reduce the number of the most related topic labels, we propose an enhanced scheme, in which the social network sites first cluster similar topics together, and then generate auxiliary information. Rigorous security and performance analyses prove that our proposed schemes are safe and effective. In addition, our experimental results built on a real Twitter dataset which demonstrates the efficacy and efficiency of our schemes.

CorrectMR: Authentication of Distributed SQL Execution on MapReduce

In this paper, we consider the SQL Selection-GroupBy-Aggregation (SGA) query evaluation on an untrusted MapReduce system in which mappers and reducers may return incorrect results. We design ${\sl CorrectMR}$ C o r r e c t M R , a system that supports efficient verification of result correctness for both intermediate and final results of SGA queries. ${\sl CorrectMR}$ C o r r e c t M R includes the design of Pedersen Merkle R-tree (PMR-tree), a new authenticated data structure (ADS). To enable efficient verification, ${\sl CorrectMR}$ C o r r e c t M R includes a distributed ADS construction mechanism that allows mappers/reducers to construct PMR-trees in parallel without a centralized party. ${\sl CorrectMR}$ C o r r e c t M R provides the following verification functionality: (1) correctness verification of PMR-trees by replication; (2) correctness verification of intermediate (final, resp.) query results by constructing local ( global , resp.) PMR-trees and verification objects. Our experimental results demonstrate the efficiency and effectiveness of ${\sl CorrectMR}$ C o r r e c t M R .

Efficient and Scalable Integrity Verification of Data and Query Results for Graph Databases

Graphs are used for representing and understanding objects and their relationships for numerous applications such as social networks, Semantic Webs, and biological networks. Integrity assurance of data and query results for graph databases is an essential security requirement. In this paper, we propose two efficient integrity verification schemes—HMACs for graphs (gHMAC) for two-party data sharing, and redactable HMACs for graphs (rgHMAC) for third-party data sharing, such as a cloud-based graph database service. We compute one HMAC value for both the schemes and two other verification objects for rgHMAC scheme that are shared with the verifier. We show that the proposed schemes are provably secure with respect to integrity attacks on the structure and/or content of graphs and query results. The proposed schemes have linear complexity in terms of the number of vertices and edges in the graphs, which is shown to be optimal. Our experimental results corroborate that the proposed HMAC-based schemes for graphs are highly efficient as compared to the digital signature-based schemes—computation of HMAC tags is about 10 times faster than the computation of digital signatures.

Security and Verification of Server Data Using Frequent Itemset Mining in Ecommerce

Frequent itemsets refer to a set of data values (e.g., product items) whose number of co-occurrences exceeds a given threshold. The challenge is that the design of proofs and verification objects has to be customized for different data mining algorithms. Intended method will implement a basic idea of completeness verification and authentication approach in which the client will uses a set of frequent item sets as the evidence, and checks whether the server has missed any frequent item set as evidence in its returned result. It will help client detect untrusted server and system will become much more efficiency by reducing time. In authentication process CaRP is both a captcha and a graphical password scheme. CaRP addresses a number of security problems altogether, such as online guessing attacks, relay attacks, and, if combined with dual-view technologies, shoulder-surfing attacks.

RkNN query integrity with influence zone

With the popularity of location-based service (LBS) and the ever-increasing usage of smart phones and tablet devices, the necessity of outsourcing spatial data has grown rapidly over the past few years. In outsourced spatial databases, the LBS provides query services to the clients on behalf of the data owner. However, the LBS provider is not always trustworthy and it may send incomplete or incorrect query results to the clients. Therefore, ensuring spatial query integrity is critical. In this paper, we propose efficient RkNN query verification techniques which utilize the influence zone to check the integrity of query results. Our methods aim to authenticate both monochromatic and bichromatic RkNN queries results. Specifically, our methods can gain efficient performance on verifying bichromatic RkNN query. Furthermore, our methods can be used to authenticate both snapshot and continuous RkNN queries. We also present a detailed theoretical analysis of the size of verification objects (VO). Extensive experiments on both real and synthetic datasets demonstrate the efficiency of our proposed authenticating methods.

Authenticated Subgraph Similarity Searchin Outsourced Graph Databases

Subgraph similarity search is used in graph databases to retrieve graphs whose subgraphs are similar to a given query graph. It has been proven successful in a wide range of applications including bioinformatics and chem-informatics, etc. Due to the cost of providing efficient similarity search services on ever-increasing graph data, database outsourcing is apparently an appealing solution to database owners. Unfortunately, query service providers may be untrusted or compromised by attacks. To our knowledge, no studies have been carried out on the authentication of the search. In this paper, we propose authentication techniques that follow the popular filtering-and-verification framework. We propose an authentication-friendly metric index called ${\tt GMTree}$ . Specifically, we transform the similarity search into a search in a graph metric space and derive small verification objects ( $\cal VO$ s) to-be-transmitted to query clients. To further optimize ${\tt GMTree}$ , we propose a sampling-based pivot selection method and an authenticated version of ${\tt MCS}$ computation. Our comprehensive experiments verified the effectiveness and efficiency of our proposed techniques.

Authentication of Moving Top-k Spatial Keyword Queries

A moving top- <inline-formula><tex-math notation="LaTeX">$k$</tex-math></inline-formula> spatial keyword (M <inline-formula> <tex-math notation="LaTeX">$k$</tex-math> </inline-formula> SK) query, which takes into account a continuously moving query location, enables a mobile client to be continuously aware of the top- <inline-formula><tex-math notation="LaTeX">$k$</tex-math></inline-formula> spatial web objects that best match a query with respect to location and text relevance. The increasing mobile use of the web and the proliferation of geo-positioning render it of interest to consider a scenario where spatial keyword search is outsourced to a separate service provider capable at handling the voluminous spatial web objects available from various sources. A key challenge is that the service provider may return inaccurate or incorrect query results (intentionally or not), e.g., due to cost considerations or invasion of hackers. Therefore, it is attractive to be able to authenticate the query results at the client side. Existing authentication techniques are either inefficient or inapplicable for the kind of query we consider. We propose new authentication data structures, the MIR-tree and MIR <inline-formula> <tex-math notation="LaTeX">$^*$</tex-math></inline-formula> -tree, that enable the authentication of MkSK queries at low computation and communication costs. We design a verification object for authenticating MkSK queries, and we provide algorithms for constructing verification objects and using these for verifying query results. A thorough experimental study on real data shows that the proposed techniques are capable of outperforming two baseline algorithms by orders of magnitude.

Towards Efficient Authenticated Subgraph Query Service in Outsourced Graph Databases

Graphs are powerful tools suitable for a large variety of applications including chemical databases and the Semantic Web, among others. A fundamental query of graph databases is the subgraph query: given a query graph q, it retrieves the data graphs from a database that contain q. Due to the cost of managing massive data coupled with the computational hardness of subgraph query processing, outsourcing the processing to a third-party service provider is an appealing alternative. However, security properties such as data integrity and the response time are critical Quality of Service (QoS) issues in query services. Unfortunately, to our knowledge, authenticated subgraph query services have not been addressed before. To support the service, we propose Merkle IFTree (MIFTree) where Merkle hash trees are applied into our Intersection-aware Feature-subgraph Tree (IFTree). IFTree aims to minimize I/O in a well-received subgraph query paradigm called the filtering-and-verification framework. The structures that need to be introduced to verification objects ( VOs) and the authentication time are both minimized. Subsequently, the overall response time is minimized. For optimizations, we propose an enhanced authentication method on MIFTree. Our detailed experiments on both real and synthetic datasets demonstrate that MIFTree is clearly more efficient than a baseline method.

Authentication of k Nearest Neighbor Query on Road Networks

Outsourcing spatial databases to the cloud provides an economical and flexible way for data owners to deliver spatial data to users of location-based services. However, in the database outsourcing paradigm, the third-party service provider is not always trustworthy, therefore, ensuring spatial query integrity is critical. In this paper, we propose an efficient road network k-nearest-neighbor query verification technique which utilizes the network Voronoi diagram and neighbors to prove the integrity of query results. Unlike previous work that verifies k-nearest-neighbor results in the Euclidean space, our approach needs to verify both the distances and the shortest paths from the query point to its kNN results on the road network. We evaluate our approach on real-world road networks together with both real and synthetic points of interest datasets. Our experiments run on Google Android mobile devices which communicate with the service provider through wireless connections. The experiment results show that our approach leads to compact verification objects (VO) and the verification algorithm on mobile devices is efficient, especially for queries with low selectivity.

Spatial Query Integrity with Voronoi Neighbors

With the popularity of location-based services and the abundant usage of smart phones and GPS-enabled devices, the necessity of outsourcing spatial data has grown rapidly over the past few years. Meanwhile, the fast arising trend of cloud storage and cloud computing services has provided a flexible and cost-effective platform for hosting data from businesses and individuals, further enabling many location-based applications. Nevertheless, in this database outsourcing paradigm, the authentication of the query results at the client remains a challenging problem. In this paper, we focus on the Outsourced Spatial Database (OSDB) model and propose an efficient scheme, called VN-Auth, which allows a client to verify the correctness and completeness of the result set. Our approach is based on neighborhood information derived from the Voronoi diagram of the underlying spatial data set and can handle fundamental spatial query types, such as k nearest neighbor and range queries, as well as more advanced query types like reverse k nearest neighbor, aggregate nearest neighbor, and spatial skyline. We evaluated VN-Auth based on real-world data sets using mobile devices (Google Droid smart phones with Android OS) as query clients. Compared to the current state-of-the-art approaches (i.e., methods based on Merkle Hash Trees), our experiments show that VN-Auth produces significantly smaller verification objects and is more computationally efficient, especially for queries with low selectivity.

A General Model for Authenticated Data Structures

Query answers from on-line databases can easily be corrupted by hackers or malicious database publishers. Thus it is important to provide mechanisms which allow clients to trust the results from on-line queries. Authentic publication allows untrusted publishers to answer securely queries from clients on behalf of trusted off-line data owners. Publishers validate answers using hard-to-forge verification objects VOs), which clients can check efficiently. This approach provides greater scalability, by making it easy to add more publishers, and better security, since on-line publishers do not need to be trusted.To make authentic publication attractive, it is important for the VOs to be small, efficient to compute, and efficient to verify. This has lead researchers to develop independently several different schemes for efficient VO computation based on specific data structures. Our goal is to develop a unifying framework for these disparate results, leading to a generalized security result. In this paper we characterize a broad class of data structures which we call Search DAGs, and we develop a generalized algorithm for the construction of VOs for Search DAGs. We prove that the VOs thus constructed are secure, and that they are efficient to compute and verify. We demonstrate how this approach easily captures existing work on simple structures such as binary trees, multi-dimensional range trees, tries, and skip lists. Once these are shown to be Search DAGs, the requisite security and efficiency results immediately follow from our general theorems. Going further, we also use Search DAGs to produce and prove the security of authenticated versions of two complex data models for efficient multi-dimensional range searches. This allows efficient VOs to be computed (size O(log N + T)) for typical one- and two-dimensional range queries, where the query answer is of size T and the database is of size N. We also show I/O-efficient schemes to construct the VOs. For a system with disk blocks of size B, we answer one-dimensional and three-sided range queries and compute the VOs with O(logB N + T/B) I/O operations using linear size data structures.