181,989 research outputs found
Privacy-Preserving Secret Shared Computations using MapReduce
Data outsourcing allows data owners to keep their data at \emph{untrusted}
clouds that do not ensure the privacy of data and/or computations. One useful
framework for fault-tolerant data processing in a distributed fashion is
MapReduce, which was developed for \emph{trusted} private clouds. This paper
presents algorithms for data outsourcing based on Shamir's secret-sharing
scheme and for executing privacy-preserving SQL queries such as count,
selection including range selection, projection, and join while using MapReduce
as an underlying programming model. Our proposed algorithms prevent an
adversary from knowing the database or the query while also preventing
output-size and access-pattern attacks. Interestingly, our algorithms do not
involve the database owner, which only creates and distributes secret-shares
once, in answering any query, and hence, the database owner also cannot learn
the query. Logically and experimentally, we evaluate the efficiency of the
algorithms on the following parameters: (\textit{i}) the number of
communication rounds (between a user and a server), (\textit{ii}) the total
amount of bit flow (between a user and a server), and (\textit{iii}) the
computational load at the user and the server.\BComment: IEEE Transactions on Dependable and Secure Computing, Accepted 01
Aug. 201
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
Pay One, Get Hundreds for Free: Reducing Cloud Costs through Shared Query Execution
Cloud-based data analysis is nowadays common practice because of the lower
system management overhead as well as the pay-as-you-go pricing model. The
pricing model, however, is not always suitable for query processing as heavy
use results in high costs. For example, in query-as-a-service systems, where
users are charged per processed byte, collections of queries accessing the same
data frequently can become expensive. The problem is compounded by the limited
options for the user to optimize query execution when using declarative
interfaces such as SQL. In this paper, we show how, without modifying existing
systems and without the involvement of the cloud provider, it is possible to
significantly reduce the overhead, and hence the cost, of query-as-a-service
systems. Our approach is based on query rewriting so that multiple concurrent
queries are combined into a single query. Our experiments show the aggregated
amount of work done by the shared execution is smaller than in a
query-at-a-time approach. Since queries are charged per byte processed, the
cost of executing a group of queries is often the same as executing a single
one of them. As an example, we demonstrate how the shared execution of the
TPC-H benchmark is up to 100x and 16x cheaper in Amazon Athena and Google
BigQuery than using a query-at-a-time approach while achieving a higher
throughput
Discrete choice modelling incorporating attribute thresholds of perception
In this paper we formulate a discrete choice model that incorporates thresholds in the
perception of attribute changes. The model considers multiple options and allows
changes in several attributes. We postulate that if thresholds exist they could be random,
differ between individuals, and even be a function of socio-economic characteristics and
choice conditions. Our formulation allows estimation of the parameters of the threshold
probability distribution starting from information about choices.
The model is applied to synthetic data and also to real data from a stated preference
survey. We found that where perception thresholds exist in the population, the use of
models without them leads to errors in estimation and prediction. Clearly, the effect is
more relevant when the typical size of change in the attribute value is comparable with
the threshold, and when the contribution of this attribute in the utility function is
substantial. Finally, we discuss the implications of the threshold model for estimation of
the benefits of transport investments, and show that where thresholds exist, models that
do not represent them can overestimate benefits substantially
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