fVSS: A New Secure and Cost-Efficient Scheme for Cloud Data Warehouses

Cloud business intelligence is an increasingly popular choice to deliver decision support capabilities via elastic, pay-per-use resources. However, data security issues are one of the top concerns when dealing with sensitive data. In this pa-per, we propose a novel approach for securing cloud data warehouses by flexible verifiable secret sharing, fVSS. Secret sharing encrypts and distributes data over several cloud ser-vice providers, thus enforcing data privacy and availability. fVSS addresses four shortcomings in existing secret sharing-based approaches. First, it allows refreshing the data ware-house when some service providers fail. Second, it allows on-line analysis processing. Third, it enforces data integrity with the help of both inner and outer signatures. Fourth, it helps users control the cost of cloud warehousing by balanc-ing the load among service providers with respect to their pricing policies. To illustrate fVSS' efficiency, we thoroughly compare it with existing secret sharing-based approaches with respect to security features, querying power and data storage and computing costs

Secret Sharing for Cloud Data Security

Cloud computing helps reduce costs, increase business agility and deploy solutions with a high return on investment for many types of applications. However, data security is of premium importance to many users and often restrains their adoption of cloud technologies. Various approaches, i.e., data encryption, anonymization, replication and verification, help enforce different facets of data security. Secret sharing is a particularly interesting cryptographic technique. Its most advanced variants indeed simultaneously enforce data privacy, availability and integrity, while allowing computation on encrypted data. The aim of this paper is thus to wholly survey secret sharing schemes with respect to data security, data access and costs in the pay-as-you-go paradigm

A Novel Multi-Secret Sharing Approach for Secure Data Warehousing and On-Line Analysis Processing in the Cloud

Republished from the International Journal of Data Warehousing and Mining, Vol. 11, No. 2, April-June 2015, 21-42International audienceCloud computing can help reduce costs, increase business agility and deploy solutions with a high return on investment for many types of applications, including data warehouses and on-line analytical processing. However, storing and transferring sensitive data into the cloud rais-es legitimate security concerns. In this paper, we propose a new multi-secret sharing approach for deploying a data warehouse in the cloud and allowing on-line analysis processing, while enforcing data privacy, integrity and availability. We first validate the relevance of our ap-proach theoretically, and then experimentally with both a simple random dataset and the Star Schema Benchmark. We also demonstrate its superiority to related, existing methods

A Novel Multi-Secret Sharing Approach for Secure Data Warehousing and On-Line Analysis Processing in the Cloud

International audienceCloud computing helps reduce costs, increase business agility and deploy solutions with a high return on investment for many types of applications, including data warehouses and on-line analytical processing. However, storing and transferring sensitive data into the cloud raises legitimate security concerns. In this paper, we propose a new multi-secret sharing approach for deploying data warehouses in the cloud and allowing on-line analysis processing, while enforcing data privacy, integrity and availability. We first validate the relevance of our approach theoretically and then experimentally with both a simple random dataset and the Star Schema Benchmark. We also demonstrate its superiority to related methods