Comparison-based computations over fully homomorphic encrypted data

Togan, Mihai; Pleşca, Cezar

doi:10.1109/iccomm.2014.6866760

Cited by 27 publications

(18 citation statements)

References 13 publications

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“…Furthermore, at each of the computing nodes we implemented both container based virtualization [17] and also installed and configured the OpenMPI framework. Furthermore, to keep safe the entire processed information, we apply an encryption layer consisting in an encrypted file system, such as Linux Unified Key Setup [9], [10]. In Figure 6, you can see our servers testbed configuration.…”

Section: Testing and Resultsmentioning

confidence: 99%

Cyber Protection of Critical Infrastructures Using Supervised Learning

Patrascu

Patriciu

2015

2015 20th International Conference on Control Systems and Computer Science

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Interconnected computing units are used more and more in our daily lives, starting from the transportation systems and ending with gas and electricity distribution, together with tenths or hundreds of systems and sensors, called critical infrastructures. In this context, cyber protection is vital because they represent one of the most important parts of a country's economy thus making them very attractive to cyber criminals or malware attacks. Even though the detection technologies for new threats have improved over time, modern malware still manage to pass even the most secure and well organized computer networks, firewalls and intrusion detection equipments, making all systems vulnerable. This is the main reason that automatic learning is used more often than any other detection algorithms as it can learn from existing attacks and prevent newer ones.In this paper we discuss the issues threatening critical infrastructures systems and propose a framework based on machine learning algorithms and game theory decision models that can be used to protect such systems. We present the results taken after implementing it using three distinct classifiers -k nearest neighbors, decision trees and support vector machines.

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Section: Testing and Resultsmentioning

confidence: 99%

Cyber Protection of Critical Infrastructures Using Supervised Learning

Patrascu

Patriciu

2015

2015 20th International Conference on Control Systems and Computer Science

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“…It would appear further processing is necessary (additional circuit depth of log 2 n) to extract the final minimum or maximum value within the output ciphertext in a step not apparently described; perhaps being recovered after decryption. The min-max algorithm described by Togan et al [23] uses a recursive strategy to combine, in the base case, a one bit integer 'greater than' comparison relation with a selection operator both expressed in polynomial form. The overall circuit depth is similar to the approach by Kocabaş, although no ciphertext packing is involved.…”

Section: Our Contributionmentioning

confidence: 99%

“…The MSB will contain the final output for the equal circuit after log 2 n levels [34]. An alternative divide-and-conquer strategy for determining integer equality with a similar multiplicative depth is described in [23], although n − 1 homomorphic multiplications are required instead of log 2 n.…”

Section: Equality Circuitmentioning

confidence: 99%

Efficient Parallel Binary Operations on Homomorphic Encrypted Real Numbers

Basilakis

Javadi

2021

IEEE Trans. Emerg. Topics Comput.

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A number of homomorphic encryption application areas, such as privacy-preserving machine learning analysis in the cloud, could be better enabled if there existed a general solution for combining sufficiently expressive logical and numerical circuit primitives to form higherlevel algorithms relevant to the application domain. Logical primitives are more efficient in a binary plaintext message space, whereas numeric primitives favour a word-based message space before encryption. In a step closer to an overall strategy of combining logical and numeric operation types, this paper examines accelerating binary operations on real numbers suitable for somewhat homomorphic encryption. A parallel solution based on SIMD can be used to efficiently perform addition, subtraction and comparison operations in a single step. The result maximises computational efficiency, memory space usage and minimises multiplicative circuit depth. Performance of these primitives and their application in min-max and sorting operations are demonstrated. In sorting real numbers, a speed up of 25-30 times is observed.

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“…In this way it is possible to install even additional software inside the container without affecting the outside operating system. Furthermore, to keep safe the entire information that is stored in containers, we apply an encryption layer consisting in an encrypted file system, such as Linux Unified Key Setup [6], [7].…”

Section: B the Anomaly Detection Systemmentioning

confidence: 99%

Cloud computing digital forensics framework for automated anomalies detection

Patrascu

Velciu

Patriciu

2015

2015 IEEE 10th Jubilee International Symposium on Applied Computational Intelligence and Informatics

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Cloud Computing is one of the most important paradigms used in today's digital environment because they offer to the user benefits such as virtual machine renting, digital information backup, ease of access to stored data and many other. Together with the increased usage of these technologies, at the datacenter level we need to know in detail the information flux between the computing nodes. More exactly, on which server the data is processed, how it is manipulated and stored at the physical or virtual level. To have a full picture of what it is going on we need to have a centralized system that can collect data regarding about the datacenters status and correlate them with known anomalies and other usage patterns and in case of a security breach to act accordingly.In this paper we present a new way to monitor running virtual machines existing at a datacenter level. We will talk about the architecture, and how we use the information collected to train our automated anomalies machine learning modules. We also present some implementation details and results taken from the experimental setup.

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Comparison-based computations over fully homomorphic encrypted data

Cited by 27 publications

References 13 publications

Cyber Protection of Critical Infrastructures Using Supervised Learning

Cyber Protection of Critical Infrastructures Using Supervised Learning

Efficient Parallel Binary Operations on Homomorphic Encrypted Real Numbers

Cloud computing digital forensics framework for automated anomalies detection

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