Huseyin Ulusoy

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Security concerns surrounding the rise of Big Data systems have stimulated myriad new Big Data security models and implementations over the past few years. A significant disadvantage shared by most of these implementations is that they customize the underlying system source code to enforce new policies, making the customizations difficult to maintain as(More)
Data storage is one of the most popular cloud services, and is therefore offered by most service providers. Among the various cloud based data storage services, key-value stores has emerged as a popular option for storing and retrieving billions of key-value pairs. Although using such cloud based key-value store services could generate many benefits,(More)
Executing data analytics tasks in MapReduce systems introduces new security and privacy concerns as the processed unstructured datasets may contain sensitive information (e.g., social security numbers, business sensitive information) at the level of individual records, and the existing file-level access control mechanisms provide all or nothing access to(More)
In a typical infrastructure-as-a-service cloud setting, different clients harness the cloud provider's services by executing virtual machines (VM). However, recent studies have shown that the cryptographic keys, the most crucial component in many of our daily used cryptographic protocols (e.g., SSL/TLS), can be extracted using cross-VM side-channel attacks.(More)
Big data will enable the development of novel services that enhance a company's market advantage, competition, or productivity. At the same time, the utilization of such a service could disclose sensitive data in the process, which raises significant privacy concerns. To protect individuals, various policies, such as the Code of Fair Information Practices,(More)
Traditional security techniques (e.g., authorization and encryption) have been extensively used in data management systems to provide security and privacy for many years. However, recent security breaches (e.g., WikiLeaks) showed that even if perfect access control is achieved, malicious insiders can still infer sensitive information and can misuse this(More)
The data processing capabilities of MapReduce systems pioneered with the on-demand scalability of cloud computing have enabled the Big Data revolution. However, the data controllers/owners worried about the privacy and accountability impact of storing their data in the cloud infrastructures as the existing cloud computing solutions provide very limited(More)
Data and computation integrity is the major concerns for the users of MapReduce systems. Most production-level MapReduce system optimistically assume that all nodes are trustworthy. Yet, even one compromised node can corrupt the integrity of final results generated by the computation. In the literature, this problem is addressed by many different(More)