arXiv subjects

Murat Kantarcioglu

Publications and source records attributed to Murat Kantarcioglu.

78 records · Page 5Linked to original sources

arXiv2018

Graph Based Proactive Secure Decomposition Algorithm for Context Dependent Attribute Based Inference Control Problem

Relational DBMSs continue to dominate the database market, and inference problem on external schema of relational DBMS's is still an important issue in terms of data privacy.Especially for the last 10 years, external schema construction for application-specific database usage has increased its independency from the conceptual schema, as the definitions and implementations of views and procedures have been optimized. This paper offers an optimized decomposition strategy for the external schema, which concentrates on the privacy policy and required associations of attributes for the intended user roles. The method proposed in this article performs a proactive decomposition of the external schema, in order to satisfy both the forbidden and required associations of attributes.Functional dependency constraints of a database schema can be represented as a graph, in which vertices are attribute sets and edges are functional dependencies. In this representation, inference problem can be defined as a process of searching a subtree in the dependency graph containing the attributes that need to be related. The optimized decomposition process aims to generate an external schema, which guarantees the prevention of the inference of the forbidden attribute sets while guaranteeing the association of the required attribute sets with a minimal loss of possible association among other attributes, if the inhibited and required attribute sets are consistent with each other. Our technique is purely proactive, and can be viewed as a normalization process. Due to the usage independency of external schema construction tools, it can be easily applied to any existing systems without rewriting data access layer of applications. Our extensive experimental analysis shows the effectiveness of this optimized proactive strategy for a wide variety of logical schema volumes.

cs.DB↗

arXiv2016

CheapSMC: A Framework to Minimize SMC Cost in Cloud

Secure multi-party computation (SMC) techniques are increasingly becoming more efficient and practical thanks to many recent novel improvements. The recent work have shown that different protocols that are implemented using different sharing mechanisms (e.g., boolean, arithmetic sharings, etc.) may have different computational and communication costs. Although there are some works that automatically mix protocols of different sharing schemes to fasten execution, none of them provide a generic optimization framework to find the cheapest mixed-protocol SMC execution for cloud deployment. In this work, we propose a generic SMC optimization framework CheapSMC that can use any mixed-protocol SMC circuit evaluation tool as a black-box to find the cheapest SMC cloud deployment option. To find the cheapest SMC protocol, CheapSMC runs one time benchmarks for the target cloud service and gathers performance statistics for basic circuit components. Using these performance statistics, optimization layer of CheapSMC runs multiple heuristics to find the cheapest mix-protocol circuit evaluation. Later on, the optimized circuit is passed to a mix-protocol SMC tool for actual executable generation. Our empirical results gathered by running different cases studies show that significant cost savings could be achieved using our optimization framework.

cs.CR↗

arXiv2014

A Distributed Framework for Scalable Search over Encrypted Documents

Nowadays, huge amount of documents are increasingly transferred to the remote servers due to the appealing features of cloud computing. On the other hand, privacy and security of the sensitive information in untrusted cloud environment is a big concern. To alleviate such concerns, encryption of sensitive data before its transfer to the cloud has become an important risk mitigation option. Encrypted storage provides protection at the expense of a significant increase in the data management complexity. For effective management, it is critical to provide efficient selective document retrieval capability on the encrypted collection. In fact, considerable amount of searchable symmetric encryption schemes have been designed in the literature to achieve this task. However, with the emergence of big data everywhere, available approaches are insufficient to address some crucial real-world problems such as scalability. In this study, we focus on practical aspects of a secure keyword search mechanism over encrypted data on a real cloud infrastructure. First, we propose a provably secure distributed index along with a parallelizable retrieval technique that can easily scale to big data. Second, we integrate authorization into the search scheme to limit the information leakage in multi-user setting where users are allowed to access only particular documents. Third, we offer efficient updates on the distributed secure index. In addition, we conduct extensive empirical analysis on a real dataset to illustrate the efficiency of the proposed practical techniques.

cs.CR↗

arXiv2013

Experiments in Information Sharing

This paper reports experimental data describing the dynamics of three key information-sharing outcomes: quantity of information shared, falsification and accuracy. The experimental design follows a formal model predicting that cooperative incentives are needed to motivate subsidiaries of large organizations to share information. Empirical reaction functions reveal how lagged values of information-sharing outcomes influence information sharing in the current round. Cooperative treatments pay bonuses to everyone if at least one individual (or subsidiary) achieves accuracy. Tournament treatments pay a single bonus to whoever achieves accuracy first. As expected, tournament incentives tend to reduce sharing, increase falsification and impair accuracy. Several surprises not predicted by the formal model emerge from the data. Conditional cooperation occurs regardless of the incentive scheme, implying that the mechanism through which incentives influence improvements in information sharing is indirect.

cs.GT↗

arXiv2012

Efficient Query Verification on Outsourced Data: A Game-Theoretic Approach

To save time and money, businesses and individuals have begun outsourcing their data and computations to cloud computing services. These entities would, however, like to ensure that the queries they request from the cloud services are being computed correctly. In this paper, we use the principles of economics and competition to vastly reduce the complexity of query verification on outsourced data. We consider two cases: First, we consider the scenario where multiple non-colluding data outsourcing services exist, and then we consider the case where only a single outsourcing service exists. Using a game theoretic model, we show that given the proper incentive structure, we can effectively deter dishonest behavior on the part of the data outsourcing services with very few computational and monetary resources. We prove that the incentive for an outsourcing service to cheat can be reduced to zero. Finally, we show that a simple verification method can achieve this reduction through extensive experimental evaluation.

cs.GT↗

arXiv2011

Secure Data Processing in a Hybrid Cloud

Cloud computing has made it possible for a user to be able to select a computing service precisely when needed. However, certain factors such as security of data and regulatory issues will impact a user's choice of using such a service. A solution to these problems is the use of a hybrid cloud that combines a user's local computing capabilities (for mission- or organization-critical tasks) with a public cloud (for less influential tasks). We foresee three challenges that must be overcome before the adoption of a hybrid cloud approach: 1) data design: How to partition relations in a hybrid cloud? The solution to this problem must account for the sensitivity of attributes in a relation as well as the workload of a user; 2) data security: How to protect a user's data in a public cloud with encryption while enabling query processing over this encrypted data? and 3) query processing: How to execute queries efficiently over both, encrypted and unencrypted data? This paper addresses these challenges and incorporates their solutions into an add-on tool for a Hadoop and Hive based cloud computing infrastructure.

cs.DC↗