A Sharding-Based Approach for Enhancing Efficiency in ISSDOs for Sharing Scattered Values

Data outsourcing is a solution aimed at addressing the security and reliability issues of data storage by ensuring professional handling of the data. The growing use of outsourcing is causing concern among users due to the lack of assurance regarding the security and reliability of data stored on servers. To address these issues, some attempts have been made to implement Secret Sharing-based Data Outsourcing (SSDO) schemes. The low efficiency of these schemes led researchers to use an index server (IS). However, IS are susceptible to frequency analysis. Bucket-Chain $B^+Tree$ ($BCB^+Tree$) was proposed to tackle the frequency analysis in the current Index Server Secret Sharing-based Data Outsourcing (ISSDO) schemes. Nevertheless, this scheme works very well when the data is discrete with a limited range. Otherwise, the scheme\u27s efficiency declines significantly as it has to store one index in each bucket and the number of buckets rises significantly, rendering the use of an IS useless. In this paper, a new data structure is proposed to store the indexes in IS to mitigate this efficiency concern. Briefly, the domain of values is divided into several segments, and indexes of values in each segment are stored inside a $Shard$. Additionally, a data outsourcing scheme has been presented based on the proposed data structure. It can withstand collaboration from up to $k-1$ dishonest servers even if they have access to the IS

Secret charing vs. encryption-based techniques for privacy preserving data mining

Privacy preserving querying and data publishing has been studied in the context of statistical databases and statistical disclosure control. Recently, large-scale data collection and integration efforts increased privacy concerns which motivated data mining researchers to investigate privacy implications of data mining and how data mining can be performed without violating privacy. In this paper, we first provide an overview of privacy preserving data mining focusing on distributed data sources, then we compare two technologies used in privacy preserving data mining. The first technology is encryption based, and it is used in earlier approaches. The second technology is secret-sharing which is recently being considered as a more efficient approach

A secure data outsourcing scheme based on Asmuth – Bloom secret sharing

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.Data outsourcing is an emerging paradigm for data management in which a database is provided as a service by third-party service providers. One of the major benefits of offering database as a service is to provide organisations, which are unable to purchase expensive hardware and software to host their databases, with efficient data storage accessible online at a cheap rate. Despite that, several issues of data confidentiality, integrity, availability and efficient indexing of users’ queries at the server side have to be addressed in the data outsourcing paradigm. Service providers have to guarantee that their clients’ data are secured against internal (insider) and external attacks. This paper briefly analyses the existing indexing schemes in data outsourcing and highlights their advantages and disadvantages. Then, this paper proposes a secure data outsourcing scheme based on Asmuth–Bloom secret sharing which tries to address the issues in data outsourcing such as data confidentiality, availability and order preservation for efficient indexing