Verifying completeness of relational query results in data publishing

Pang, HweeHwa; Jain, Arpit; Ramamritham, Krithi; Tan, Kian-Lee

doi:10.1145/1066157.1066204

Cited by 213 publications

(206 citation statements)

References 22 publications

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“…Nevertheless, the solution of [22] can be applied in conjunction with the proposed methods to avoid disclosure of boundary records, when the outsourced database must comply with certain access control policies. The proofs of soundness and completeness for initial result computation are identical to those of the MB-Tree [15] and omitted.…”

Section: Initial Results Computationmentioning

confidence: 99%

“…According to the signature chaining technique [20,22], the DO produces one signature for every triplet of adjacent tuples (sorted on their query attribute values). It then transmits its data set and signatures to the SP.…”

Section: Related Workmentioning

confidence: 99%

“…This technique condenses multiple signatures into a single one, which can be verified almost as fast as an individual signature. Pang et al [22] propose a solution for avoiding disclosure of boundary records, when the outsourced database must comply with certain access control policies.…”

Section: Related Workmentioning

confidence: 99%

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Continuous authentication on relational streams

2009

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According to the database outsourcing model, a data owner delegates database functionality to a thirdparty service provider, which answers queries received from clients. Authenticated query processing enables the clients to verify the correctness of query results. Despite the abundance of methods for authenticated processing in conventional databases, there is limited work on outsourced data streams. Stream environments pose new challenges such as the need for fast structure updating, support for continuous query processing and authentication, and provision for temporal completeness. Specifically, in addition to the correctness of individual results, the client must be able to verify that there are no missing results in between data updates. This paper presents a comprehensive set of methods covering relational streams. We first describe REF, a technique that achieves correctness and temporal completeness but incurs false transmissions, i.e., the provider has to inform the clients whenever there is a data update, even if their results are not affected. Then, we propose CADS, which minimizes the processing and transmission overhead through an elaborate indexing scheme and a virtual caching mechanism. In addition, we present an analytical study to determine the optimal indexing granularity, and extend CADS for the case that the data distribution changes over time. Finally, we evaluate the effectiveness of our techniques through extensive experiments.

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Section: Initial Results Computationmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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Continuous authentication on relational streams

2009

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“…At a high level, existing solutions addressing this problem can be divided into two main classes: deterministic and probabilistic. Deterministic approaches are based on the definition of authenticated data structures, which are structures built over specific attributes (e.g., Merkle hash trees or signature chaining schemas [38,39]). A user submits a query to a cloud provider that executes it and returns the query result along with the information necessary for the user to verify the correctness and completeness of the query result.…”

Section: Integrity Of Computationsmentioning

confidence: 99%

Data Security Issues in Cloud Scenarios

Vimercati

Foresti

Samarati

2015

Information Systems Security

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Abstract. The amount of data created, stored, and processed has enormously increased in the last years. Today, millions of devices are connected to the Internet and generate a huge amount of (personal) data that need to be stored and processed using scalable, e cient, and reliable computing infrastructures. Cloud computing technology can be used to respond to these needs. Although cloud computing brings many benefits to users and companies, security concerns about the cloud still represent the major impediment for its wide adoption. We briefly survey the main challenges related to the storage and processing of data in the cloud. In particular, we focus on the problem of protecting data in storage, supporting fine-grained access, selectively sharing data, protecting query privacy, and verifying the integrity of computations.

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“…For simplicity, we consider the LBS as the adversary, but ILC is safe against any of the aforementioned types of entities. Note that ensuring the authenticity of the POIs reported to the users is outside the scope of this paper; result verification methods (e.g., [20][21][22]) could be used in conjunction with ILC to detect any man-in-the-middle tampering with the results.…”

Section: Assumptionsmentioning

confidence: 99%

Spatial Cloaking Revisited: Distinguishing Information Leakage from Anonymity

Tan

Lin

Mouratidis

2009

Advances in Spatial and Temporal Databases

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Abstract. Location-based services (LBS) are receiving increasing popularity as they provide convenience to mobile users with on-demand information. The use of these services, however, poses privacy issues as the user locations and queries are exposed to untrusted LBSs. Spatial cloaking techniques provide privacy in the form of k-anonymity; i.e., they guarantee that the (location of the) querying user u is indistinguishable from at least k-1 others, where k is a parameter specified by u at query time. To achieve this, they form a group of k users, including u, and forward their minimum bounding rectangle (termed anonymizing spatial region, ASR) to the LBS. The rationale behind sending an ASR instead of the distinct k locations is that exact user positions (querying or not) should not be disclosed to the LBS. This results in large ASRs with considerable dead-space, and leads to unnecessary performance degradation. Additionally, there is no guarantee regarding the amount of location information that is actually revealed to the LBS. In this paper, we introduce the concept of information leakage in spatial cloaking. We provide measures of this leakage, and show how we can trade it for better performance in a tunable manner. The proposed methodology directly applies to centralized and decentralized cloaking models, and is readily deployable on existing systems.

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Verifying completeness of relational query results in data publishing

Cited by 213 publications

References 22 publications

Continuous authentication on relational streams

Continuous authentication on relational streams

Data Security Issues in Cloud Scenarios

Spatial Cloaking Revisited: Distinguishing Information Leakage from Anonymity

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