Exploring The Application Of Homomorphic Encryption For A Cross Domain Solution
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Exploring the Application of Homomorphic Encryption for a Cross Domain Solution
"A cross domain solution is a means of information assurance that provides the ability to access or transfer digital data between varying security domains. Most acceptable cross domain solutions focus mainly on risk management policies that rely on using protected or trusted parties to handle the information in order to solve this problem; thus, a cross domain solution that is able to function in the presence of untrusted parties is an open problem. Homomorphic encryption is a type of encryption that allows its party members to operate and evaluate encrypted data without the need to decrypt it. Practical homomorphic encryption is an emerging technology that may propose a solution to the unsolved problem of cross domain routing without leaking information as well as many other unique scenarios. However, despite much advancement in research, current homomorphic schemes still challenge to achieve high performance. Thus, the plausibility of its implementation relies on the requirements of the tailored application. We apply the concepts of homomorphic encryption to explore a new solution in the context of a cross domain problem. We built a practical software case study application using the YASHE fully homomorphic scheme around the specific challenge of evaluating the gateway bypass condition on encrypted data. Next, we assess the plausibility of such an application through memory and performance profiling in order to find an optimal parameter selection that ensures proper homomorphic evaluation. The correctness of the application was assured for a 64-bit security parameter selection of YASHE resulting in high latency performance. However, literature has shown that the high latency performance can be heavily mitigated through use of hardware accelerators. Other configurations that include reducing number of SIMON rounds or avoiding the homomorphic SIMON evaluation completely were explored that show more promising performance results but either at the cost of security or network bandwidth."--Abstract.
Fully Homomorphic Encryption in Real World Applications
This book explores the latest developments in fully homomorphic encryption (FHE), an effective means of performing arbitrary operations on encrypted data before storing it in the ‘cloud’. The book begins by addressing perennial problems like sorting and searching through FHE data, followed by a detailed discussion of the basic components of any algorithm and adapting them to handle FHE data. In turn, the book focuses on algorithms in both non-recursive and recursive versions and discusses their realizations and challenges while operating in the FHE domain on existing unencrypted processors. It highlights potential complications and proposes solutions for encrypted database design with complex queries, including the basic design details of an encrypted processor architecture to support FHE operations in real-world applications.
Homomorphic Encryption for Data Science (HE4DS)
This book provides basic knowledge required by an application developer to understand and use the Fully Homomorphic Encryption (FHE) technology for privacy preserving Data-Science applications. The authors present various techniques to leverage the unique features of FHE and to overcome its characteristic limitations. Specifically, this book summarizes polynomial approximation techniques used by FHE applications and various data packing schemes based on a data structure called tile tensors, and demonstrates how to use the studied techniques in several specific privacy preserving applications. Examples and exercises are also included throughout this book. The proliferation of practical FHE technology has triggered a wide interest in the field and a common wish to experience and understand it. This book aims to simplify the FHE world for those who are interested in privacy preserving data science tasks, and for an audience that does not necessarily have a deep cryptographic background, including undergraduate and graduate-level students in computer science, and data scientists who plan to work on private data and models.