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Abstract.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.

This paper attempts to introduce a method for developing secure software based on the vulnerabilities which are already known. In the proposed method, the most prevalent vulnerabilities are selected. For each vulnerability its location of appearance within the software development process, as well as methods of mitigation through design-level or implementationlevel activities is discussed. Mapping vulnerabilities to design and implementation within software development process not only results to a better understanding of vulnerability emergence, but also allows countermeasures to be applied during initial steps of vulnerability creation, and thus better software security. This mapping shows that choosing a suitable programming language and enforcing the least privileges are the most vital design time decisions. Also, security code review and server side input validation are implementation-level activities assumed to cover most of the vulnerabilities.

Privacy preservation is an important issue in data publishing. Existing approaches on privacy-preserving data publishing rely on tabular anonymization techniques such as k-anonymity, which do not provide appropriate results for aggregate queries. The solutions based on graph anonymization have also been proposed for relational data to hide only bipartite relations. In this paper, we propose an approach for anonymizing multirelation constraints (ternary or more) with (t,k) hypergraph anonymization in data publishing. To this end, we model constraints as undirected hypergraphs and formally cluster attribute relations as hyperedge with the t-means-clustering algorithm. In addition, anonymization is carried out with a k-anonymity method in every cluster for which the parameter k can vary in each cluster, to attain more flexibility and less information loss with respect to utility. Our experiments demonstrate that this approach offers a great trade-off between privacy and utility.