text: 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 paper, 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 service providers, thus enforcing data privacy and availability. fVSS addresses four shortcomings in existing secret sharing-based approaches. First, it allows refreshing the data warehouse 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 balancing 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.
  @cite_18: Security of database outsourcing, due to the untrustworthiness of service provider, is a basic challenge to have Database As a Service in a cloud computing environment. Having disparate assumptions to solve different aspects of security such as confidentiality and integrity is an obstacle for an integrated secure solution through the combination of existing approaches. Concentrating on confidentiality and integrity aspects of database outsourcing, this paper proposes an approach in which each attribute value is split up between several data servers using a customized threshold secret sharing scheme. Our approach preserves data confidentiality and at the same time provides the correctness verifiability of query results for clients. The distribution algorithm and redundant shares in the secret sharing scheme are the basis of correctness verification for query results.
  @cite_1: Data integration from multiple autonomous data sources has emerged as an important practical problem. The key requirement for such data integration is that owners of such data need to cooperate in a competitive landscape in most of the cases. The research challenge in developing a query processing solution is that the answers to the queries need to be provided while preserving the privacy of the data sources. In general, allowing unrestricted read access to the whole data may give rise to potential vulnerabilities as well as may have legal implications. Therefore, there is a need for privacy preserving database operations for querying data residing at different parties. In this paper, we propose a new query processing technique using third parties in a peer-to-peer system. We propose and evaluate two different protocols for various database operations. Our scheme is able to answer queries without revealing any useful information to the data sources or to the third parties. Analytical comparison of the proposed approach with other recent proposals for privacy-preserving data integration establishes the superiority of the proposed approach in terms of query response time
  @cite_17: In this paper we show how to divide data D into n pieces in such a way that D is easily reconstructable from any k pieces, but even complete knowledge of k - 1 pieces reveals absolutely no information about D . This technique enables the construction of robust key management schemes for cryptographic systems that can function securely and reliably even when misfortunes destroy half the pieces and security breaches expose all but one of the remaining pieces.
  @cite_6: Recent trends in the global economy force competitive enterprises to collaborate with each other to analyze markets in a better way and make decisions based on that. Therefore, they might want to share their data with each other to run data mining algorithms over the union of their data to get more accurate and representative results. During this process they do not want to reveal their data to each other due to the legal issues and competition. However, current systems do not consider privacy preservation in data sharing across private data sources. To satisfy this requirement, we propose a distributed middleware, ABACUS, to perform intersection, join, and aggregation queries over multiple private data warehouses in a privacy preserving manner. Our analytical evaluations show that ABACUS is efficient and scalable.
  @cite_3: Researchers have been studying security challenges of database outsourcing for almost a decade. Privacy of outsourced data is one of the main challenges when the “Database As a Service” model is adopted in the service oriented trend of the cloud computing paradigm. This is due to the insecurity of the network environment or even the untrustworthiness of the service providers. This paper proposes a method to preserve privacy of outsourced data based on Shamir’s secret sharing scheme. We split attribute values into several parts and distribute them among untrusted servers. The problem of using secret sharing in data outsourcing scenario is how to search efficiently within the randomly generated pool of shares. In this paper, at first, we customize Shamir’s scheme to have A Searchable Secret Sharing Scheme (AS4) that enables the efficient execution of different kinds of queries over distributed shares. Then, we extend our method for sharing values to A Secure Searchable Secret Sharing Scheme (AS5) to tolerate statistical attacks based on adversary’s knowledge about outsourced data distribution. In AS5 data shares are generated uniformly across a domain to prevent information leakage about the outsourced data.
  @cite_15: Data outsourcing or database as a service is a new paradigm for data management in which a third party service provider hosts a database as a service. The service provides data management for its customers and thus obviates the need for the service user to purchase expensive hardware and software, deal with software upgrades and hire professionals for administrative and maintenance tasks. Since using an external database service promises reliable data storage at a low cost it is very attractive for companies. Such a service would also provide universal access, through the Internet to private data storedat reliable and secure sites. A client would store their data, and not need to carry their data with them as they travel. They would also not need to log remotely to their home machines, which may suffer from crashes and be unavailable. However, recent governmental legislations, competition among companies, and database thefts mandate companies to use secure and privacy preserving data management techniques. The data provider, therefore, needs to guarantee that the data is secure, be able to execute queries on the data, and the results of the queries must also be secure and not visible to the data provider. Current research has been focused only on how to index and query encrypted data. However, querying encrypted data is computationally very expensive. to push both database service providers and clients to behave honestly has emerged as one of the most important problem before data outsourcing to become a viable paradigm. In this paper, we describe scalable privacy preserving algorithms for data outsourcing. Instead of encryption, which is computationally expensive, we use distribution on multiple data provider sites and information theoretically proven secret sharing algorithms as the basis for privacy preserving outsourcing. The technical contributions of this paper is the establishment and development of a framework for efficient fault-tolerant scalable and theoretically secure privacy preserving data outsourcing that supports a diversity of database operations executed on different types of data, which can even leverage publicly available data sets.
  @cite_11: 
summary: Regarding accessing shares, classical secret sharing @cite_17 and most of its above-cited extensions natively support some exact match and aggregation operators, i.e., equality and inequality, sum, average and count. Each approach handles operators needing sorted data, e.g., range operators, maximum and minimum, with various techniques: preaggregation before sharing @cite_6 , a B++ tree index @cite_11 @cite_18 or the rank coefficient used in classical secret sharing @cite_15 @cite_1 @cite_3 . However, extra storage space is needed to store these data structures. Finally, almost all approaches allow updates on shares, since each piece of data is encrypted independently.