When Can We Answer Queries Using Result-Bounded Data Interfaces?

Amarilli, Antoine; Benedikt, Michael

doi:10.1145/3196959.3196965

Cited by 11 publications

(21 citation statements)

References 37 publications

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“…In the context of relational databases, several approaches have been proposed for enabling secure collaborative computations over data. Traditional solutions can be based on the definition of views (e.g., [35,46]), representing portions of a dataset for which different subjects are authorized, or on access patterns (e.g., [3,5]), restricting the data that can be accessed based on the input that is provided by a subject. Such solutions however only capture the information that is explicitly involved in the computation, while a computation may also leak information not explicitly visible.…”

Section: Collaborative Computationsmentioning

confidence: 99%

Digital infrastructure policies for data security and privacy in smart cities

Vimercati

Foresti

Livraga

et al. 2022

Smart Cities Policies and Financing

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The availability of large amounts of data is vital for the working of smart cities, which need to process heterogeneous information on citizens and the surrounding environment for enabling smart services. Since those data collections can include personal/sensitive information, ensuring security and privacy of the data collected and produced in a smart city is a key problem to be addressed. Two of the main pillars for protecting data privacy and security are anonymization and encryption, which however need to be carefully designed and adopted for ensuring effective protection while not compromising the possibility of performing analysis, a central aspect in smart cities. In this chapter, we address the problem of protecting large data collections in the context of smart cities, and illustrate possible approaches for effectively anonymizing data and for encrypting them, while permitting to perform computations.

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Section: Collaborative Computationsmentioning

confidence: 99%

Digital infrastructure policies for data security and privacy in smart cities

Vimercati

Foresti

Livraga

et al. 2022

Smart Cities Policies and Financing

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“…Towards our characterization, we combine a technique known as linearization [18], which converts a set of guarded TGDs into a set of linear TGDs, with simplification used in the previous section. Linearization is a very useful technique that has found several applications in the context of query answering [2,4,5,18,19]. This is the first time, however, that it is used in the context of chase termination.…”

Section: Guarded Tgdsmentioning

confidence: 99%

Non-Uniformly Terminating Chase: Size and Complexity

Gottlob¹,

Pieris²

2022

Preprint

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The chase procedure, originally introduced for checking implication of database constraints, and later on used for computing data exchange solutions, has recently become a central algorithmic tool in rule-based ontological reasoning. In this context, a key problem is non-uniform chase termination: does the chase of a database w.r.t. a rule-based ontology terminate? And if this is the case, what is the size of the result of the chase? We focus on guarded tuplegenerating dependencies (TGDs), which form a robust rule-based ontology language, and study the above central questions for the semi-oblivious version of the chase. One of our main findings is that non-uniform semi-oblivious chase termination for guarded TGDs is feasible in polynomial time w.r.t. the database, and the size of the result of the chase (whenever is finite) is linear w.r.t. the database. Towards our results concerning non-uniform chase termination, we show that basic techniques such as simplification and linearization, originally introduced in the context of ontological query answering, can be safely applied to the chase termination problem.

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“…The problem of managing queries in distributed scenarios has been extensively studied, but traditional solutions (e.g., [19,21]) as well as modern approaches that consider big data analytics (e.g., [2,4,25]) do not take into consideration access restrictions. In the relational database context, access restrictions can be supported by views (e.g., [9,17,26]), access patterns (e.g., [3,6]), or data masking (e.g., [20]). Such proposals however do not consider encryption.…”

Section: Related Workmentioning

confidence: 99%

An authorization model for query execution in the cloud

et al. 2021

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We present a novel approach for the specification and enforcement of authorizations that enables controlled data sharing for collaborative queries in the cloud. Data authorities can establish authorizations regulating access to their data distinguishing three visibility levels (no visibility, encrypted visibility, and plaintext visibility). Authorizations are enforced accounting for the information content carried in the computation to ensure no information is improperly leaked and adjusting visibility of data on-the-fly. Assignment of operations to subjects takes into consideration the cost of operation execution as well as of the encryption/decryption operations needed to make the assignment authorized. Our approach enables users and data authorities to fully enjoy the benefits and economic savings of the competitive open cloud market, while maintaining control over data.

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When Can We Answer Queries Using Result-Bounded Data Interfaces?

Cited by 11 publications

References 37 publications

Digital infrastructure policies for data security and privacy in smart cities

Digital infrastructure policies for data security and privacy in smart cities

Non-Uniformly Terminating Chase: Size and Complexity

An authorization model for query execution in the cloud

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