An Encryption Methodology for Enabling the Use of Data Warehouses on the Cloud

2021 ◽  
pp. 528-559
Author(s):  
Claudivan Cruz Lopes ◽  
Valéria Cesário-Times ◽  
Stan Matwin ◽  
Cristina Dutra de Aguiar Ciferri ◽  
Ricardo Rodrigues Ciferri
Keyword(s):  
Data Warehouse ◽  
Homomorphic Encryption ◽  
Distributed Database ◽  
Pareto Optimal ◽  
Symmetric Encryption ◽  
Data Confidentiality ◽  
Cloud Data ◽  
Use Of Data ◽  
Star Schema ◽  
Definition Of

A cloud data warehouse (cloud DW) is a subject-oriented, integrated, time-variant, voluminous, nonvolatile and multidimensional distributed database that is hosted in a cloud. A solution to ensure data confidentiality for a cloud DW is cryptography. In this article, the authors propose an encryption methodology for a cloud DW stored according to the star schema, considering both the data confidentiality maintenance of the DW and the capability of processing analytical queries directly over the encrypted DW. The proposed encryption methodology comprises an encryption strategy for DW called MV-HO (MultiValued and HOmomorphic) for the definition of how the different types of DW's attributes must be encrypted. The proposed MV-HO encryption strategy was compared with encryption strategies based on symmetric encryption, order preserving symmetric encryption and homomorphic encryption. Results indicated that MV-HO is the best solution found, as MV-HO is pareto-optimal with respect to other strategies investigated.

An Encryption Methodology for Enabling the Use of Data Warehouses on the Cloud

2018 ◽  
Vol 14 (4) ◽  
pp. 38-66 ◽  
Author(s):  
Claudivan Cruz Lopes ◽  
Valéria Cesário-Times ◽  
Stan Matwin ◽  
Cristina Dutra de Aguiar Ciferri ◽  
Ricardo Rodrigues Ciferri
Keyword(s):  
Data Warehouse ◽  
Homomorphic Encryption ◽  
Distributed Database ◽  
Symmetric Encryption ◽  
Data Confidentiality ◽  
Cloud Data ◽  
Use Of Data ◽  
Star Schema ◽  
Different Types ◽  
Definition Of

A cloud data warehouse (cloud DW) is a subject-oriented, integrated, time-variant, voluminous, nonvolatile and multidimensional distributed database that is hosted in a cloud. A solution to ensure data confidentiality for a cloud DW is cryptography. In this article, the authors propose an encryption methodology for a cloud DW stored according to the star schema, considering both the data confidentiality maintenance of the DW and the capability of processing analytical queries directly over the encrypted DW. The proposed encryption methodology comprises an encryption strategy for DW called MV-HO (MultiValued and HOmomorphic) for the definition of how the different types of DW's attributes must be encrypted. The proposed MV-HO encryption strategy was compared with encryption strategies based on symmetric encryption, order preserving symmetric encryption and homomorphic encryption. Results indicated that MV-HO is the best solution found, as MV-HO is pareto-optimal with respect to other strategies investigated.

scholarly journals Forward and Backward-Secure Range-Searchable Symmetric Encryption

2021 ◽  
Vol 2022 (1) ◽  
pp. 28-48
Author(s):  
Jiafan Wang ◽  
Sherman S. M. Chow
Keyword(s):  
Homomorphic Encryption ◽  
Inverted Index ◽  
Range Queries ◽  
Symmetric Encryption ◽  
Forward Security ◽  
Injection Attacks ◽  
Searchable Symmetric Encryption ◽  
Encrypted Database ◽  
Definition Of ◽  
Client Side

Abstract Dynamic searchable symmetric encryption (DSSE) allows a client to query or update an outsourced encrypted database. Range queries are commonly needed. Previous range-searchable schemes either do not support updates natively (SIGMOD’16) or use file indexes of many long bit-vectors for distinct keywords, which only support toggling updates via homomorphically flipping the presence bit. (ESORICS’18). We propose a generic upgrade of any (inverted-index) DSSE to support range queries (a.k.a. range DSSE), without homomorphic encryption, and a specific instantiation with a new trade-off reducing client-side storage. Our schemes achieve forward security, an important property that mitigates file injection attacks. Moreover, we identify a variant of injection attacks against the first somewhat dynamic scheme (ESORICS’18). We also extend the definition of backward security to range DSSE and show that our schemes are compatible with a generic upgrade of backward security (CCS’17). We comprehensively analyze the computation and communication overheads, including implementation details of client-side index-related operations omitted by prior schemes. We show high empirical efficiency for million-scale databases over a million-scale keyword space.

scholarly journals Data Confidentiality in Cloud using Multi-party Computation

2020 ◽  
Vol 9 (3) ◽  
pp. 58-62
Keyword(s):  
Homomorphic Encryption ◽  
Input Function ◽  
Third Parties ◽  
Current Paper ◽  
Public Key ◽  
Data Confidentiality ◽  
New Era ◽  
Cloud Data

A new era has approached where we are storing our information in cloud and performing several computations on powerful servers remotely. In cloud, data is not completely secured and sometimes under the control of untrusted Third parties. Some secured protocols are being implemented. The secure multi-party computation protocol, which is existing, demands the inputs to be encrypted using a public key. So, these reasons limit this Secure Multi-party computation to be employed. In the current paper, we put forward a protocol named homomorphic encryption where the input function is being encrypted by different key. This paper also uses Multi-party computation which is one of the most secured technique in cryptography

Facilitate Effective Decision-Making by Warehousing Reduced Data

2015 ◽  
Vol 7 (3) ◽  
pp. 36-64 ◽  
Author(s):  
Faten Atigui ◽  
Franck Ravat ◽  
Jiefu Song ◽  
Olivier Teste ◽  
Gilles Zurfluh
Keyword(s):  
Decision Making ◽  
Data Warehouse ◽  
Conceptual Modeling ◽  
Multidimensional Data ◽  
Meta Model ◽  
Star Schema ◽  
Effective Decision ◽  
Effective Decision Making ◽  
Definition Of ◽  
Reduced Data

The authors' aim is to provide a solution for multidimensional data warehouse's reduction based on analysts' needs which will specify aggregated schema applicable over a period of time as well as retain only useful data for decision support. Firstly, they describe a conceptual modeling for multidimensional data warehouse. A multidimensional data warehouse's schema is composed of a set of states. Each state is defined as a star schema composed of one fact and its related dimensions. The derivation between states is carried out through combination of reduction operators. Secondly, they present a meta-model which allows managing different states of multidimensional data warehouse. The definition of reduced and unreduced multidimensional data warehouse schema can be carried out by instantiating the meta-model. Finally, they describe their experimental assessments and discuss their results. Evaluating their solution implies executing different queries in various contexts: unreduced single fact table, unreduced relational star schema, reduced star schema and reduced snowflake schema. The authors show that queries are more efficiently calculated within a reduced star schema.

Low Size Cipher Text Homomorphic Encryption Scheme for Cloud Data

2017 ◽  
Vol 4 (1) ◽  
pp. 13-20
Author(s):  
Manish M. Potey ◽  
◽  
C. A. Dhote ◽  
Deepak H. Sharma ◽  
◽  
...  
Keyword(s):  
Homomorphic Encryption ◽  
Encryption Scheme ◽  
Cloud Data ◽  
Cipher Text

Embracing School Counselors’ Situatedness: Data-Based Decision Making as Fulfillment of a Complex Identity

2021 ◽  
Vol 24 (1_part_3) ◽  
pp. 2156759X2110119
Author(s):  
Brett Zyromski ◽  
Catherine Griffith ◽  
Jihyeon Choi
Keyword(s):  
Decision Making ◽  
School Counselors ◽  
School Counselor ◽  
Use Of Data ◽  
Counselor Identity ◽  
Using Data ◽  
Definition Of ◽  
Support Students ◽  
Students Success ◽  
Data Based Decision Making

Since at least the 1930s, school counselors have used data to inform school counseling programming. However, the evolving complexity of school counselors’ identity calls for an updated understanding of the use of data. We offer an expanded definition of data-based decision making that reflects the purpose of using data in educational settings and an appreciation of the complexity of the school counselor identity. We discuss implications for applying the data-based decision-making process using a multifaceted school counselor identity lens to support students’ success.

Universal Data Warehousing Based on a Meta-Data Modeling Approach

2003 ◽  
Vol 12 (03) ◽  
pp. 325-363 ◽  
Author(s):  
Joseph Fong ◽  
Qing Li ◽  
Shi-Ming Huang
Keyword(s):  
Data Warehouse ◽  
Data Warehousing ◽  
Object Oriented ◽  
Data Models ◽  
Heterogeneous Databases ◽  
Materialized Views ◽  
Prototype System ◽  
Relational View ◽  
Metadata Model ◽  
Star Schema

Data warehouse contains vast amount of data to support complex queries of various Decision Support Systems (DSSs). It needs to store materialized views of data, which must be available consistently and instantaneously. Using a frame metadata model, this paper presents an architecture of a universal data warehousing with different data models. The frame metadata model represents the metadata of a data warehouse, which structures an application domain into classes, and integrates schemas of heterogeneous databases by capturing their semantics. A star schema is derived from user requirements based on the integrated schema, catalogued in the metadata, which stores the schema of relational database (RDB) and object-oriented database (OODB). Data materialization between RDB and OODB is achieved by unloading source database into sequential file and reloading into target database, through which an object relational view can be defined so as to allow the users to obtain the same warehouse view in different data models simultaneously. We describe our procedures of building the relational view of star schema by multidimensional SQL query, and the object oriented view of the data warehouse by Online Analytical Processing (OLAP) through method call, derived from the integrated schema. To validate our work, an application prototype system has been developed in a product sales data warehousing domain based on this approach.

scholarly journals Semantically Secure Symmetric Encryption with Error Correction for Distributed Storage

2017 ◽  
Vol 2017 ◽  
pp. 1-10
Author(s):  
Juha Partala
Keyword(s):  
Error Correction ◽  
Network Coding ◽  
Linear Combination ◽  
Distributed Storage ◽  
Homomorphic Encryption ◽  
Error Correcting Codes ◽  
Symmetric Encryption ◽  
Goppa Codes ◽  
Linear Network ◽  
Linear Network Coding

A distributed storage system (DSS) is a fundamental building block in many distributed applications. It applies linear network coding to achieve an optimal tradeoff between storage and repair bandwidth when node failures occur. Additively homomorphic encryption is compatible with linear network coding. The homomorphic property ensures that a linear combination of ciphertext messages decrypts to the same linear combination of the corresponding plaintext messages. In this paper, we construct a linearly homomorphic symmetric encryption scheme that is designed for a DSS. Our proposal provides simultaneous encryption and error correction by applying linear error correcting codes. We show its IND-CPA security for a limited number of messages based on binary Goppa codes and the following assumption: when dividing a scrambled generator matrix G^ into two parts G1^ and G2^, it is infeasible to distinguish G2^ from random and to find a statistical connection between G1^ and G2^. Our infeasibility assumptions are closely related to those underlying the McEliece public key cryptosystem but are considerably weaker. We believe that the proposed problem has independent cryptographic interest.

scholarly journals Dimensional Modeling Using Star Schema for Data Warehouse Creation

2017 ◽  
Vol 10 (04) ◽  
pp. 745-754
Author(s):  
Mudasir M Kirmani
Keyword(s):  
Data Warehouse ◽  
Transformation Model ◽  
Data Warehouses ◽  
Design Technique ◽  
End User ◽  
Conversion Model ◽  
Know How ◽  
Dimensional Modeling ◽  
Star Schema ◽  
Logical Design

Data Warehouse design requires a radical rebuilding of tremendous measures of information, frequently of questionable or conflicting quality, drawn from various heterogeneous sources. Data Warehouse configuration assimilates business learning and innovation know-how. The outline of theData Warehouse requires a profound comprehension of the business forms in detail. The principle point of this exploration paper is to contemplate and investigate the transformation model to change over the E-R outlines to Star Schema for developing Data Warehouses. The Dimensional modelling is a logical design technique used for data warehouses. This research paper addresses various potential differences between the two techniques and highlights the advantages of using dimensional modelling along with disadvantages as well. Dimensional Modelling is one of the popular techniques for databases that are designed keeping in mind the queries from end-user in a data warehouse. In this paper the focus has been on Star Schema, which basically comprises of Fact table and Dimension tables. Each fact table further comprises of foreign keys of various dimensions and measures and degenerate dimensions if any. We also discuss the possibilities of deployment and acceptance of Conversion Model (CM) to provide the details of fact table and dimension tables according to the local needs. It will also highlight to why dimensional modelling is preferred over E-R modelling when creating data warehouse.

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