Breaking MPC implementations through compression

Resende, João S.; Sousa, Patrícia R.; Martins, Rolando; Antunes, Luís

doi:10.1007/s10207-018-0424-2

Cited by 5 publications

(5 citation statements)

References 18 publications

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“…Kolmogorov Complexity focus on the absence of need for in-depth knowledge of the domain data, but this can induce users on trying to use it as a quick fix. However, from our perspective, many times it is necessary to perform preprocessing data such as the authors that analyze the security of MPC [ 43 ]. To perform validations, it was necessary to remove the entire network layer from wireshark captures, because compression should not detect significant differences in non-MPC data.…”

Section: Discussionmentioning

confidence: 99%

“… Theory to practice: There are some protocols that enforce privacy and security in communications between users. With the emergence of new cyberattacks and with the recent developments in academia regarding privacy policies imposed by rules to protect user privacy, such as GDPR, it is of utmost important to validate existing implementations to ensure user privacy [ 43 ]. …”

Section: Kolmogorov Complexity Application Scenariosmentioning

confidence: 99%

“…NCD was introduced as a novel approach to solve these problems. An example of this is the implementation analysis of a cryptographic protocol (Multiparty Computation (MPC)), where Kolmogorov Complexity helps to detect anomaly patterns in the properties that are guaranteed by the theory of the protocol [ 43 ].…”

Section: Kolmogorov Complexity Application Scenariosmentioning

confidence: 99%

See 2 more Smart Citations

A Survey on Using Kolmogorov Complexity in Cybersecurity

Resende

Martins

Antunes

2019

Entropy

Self Cite

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Security and privacy concerns are challenging the way users interact with devices. The number of devices connected to a home or enterprise network increases every day. Nowadays, the security of information systems is relevant as user information is constantly being shared and moving in the cloud; however, there are still many problems such as, unsecured web interfaces, weak authentication, insecure networks, lack of encryption, among others, that make services insecure. The software implementations that are currently deployed in companies should have updates and control, as cybersecurity threats increasingly appearing over time. There is already some research towards solutions and methods to predict new attacks or classify variants of previous known attacks, such as (algorithmic) information theory. This survey combines all relevant applications of this topic (also known as Kolmogorov Complexity) in the security and privacy domains. The use of Kolmogorov-based approaches is resource-focused without the need for specific knowledge of the topic under analysis. We have defined a taxonomy with already existing work to classify their different application areas and open up new research questions.

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Section: Discussionmentioning

confidence: 99%

Section: Kolmogorov Complexity Application Scenariosmentioning

confidence: 99%

Section: Kolmogorov Complexity Application Scenariosmentioning

confidence: 99%

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A Survey on Using Kolmogorov Complexity in Cybersecurity

Resende

Martins

Antunes

2019

Entropy

Self Cite

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“…Penelitian ini dilakukan untuk meningkatkan kapasitas informasi pada kode QR dengan memanfaatkan metode kompresi. Kompresi berarti pengkodean informasi menggunakan bit lebih sedikit daripada representasi asli untuk mengurangi konsumsi sumber daya, seperti penyimpanan dan bandwidth atau bahkan untuk alasan keamanan [16]- [18]. Kompresi dapat berupa lossy atau lossless.…”

Section: Pendahuluanunclassified

Penerapan Metode Kompresi Huffman untuk Meningkatkan Jumlah Data Informasi pada Kode Quick Response

Wang¹,

Asshobry²

2022

Journal of Information System

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Kecepatan memperoleh informasi merupakan hal yang sangat penting. Pemanfaatan kode Quick Response (QR) menjadi salah satu cara dalam upaya memperoleh informasi secara cepat. Namun, setiap versi kode QR Code sendiri memiliki batasan data informasi yang dapat ditampung, sehingga umumnya kode QR hanya menampung tautan website agar informasinya diperoleh melalui website. Peningkatan daya tampung data pada kode QR tentunya dapat memudahkan pengguna untuk menyimpan data informasi lebih banyak sehingga informasi dapat diperoleh tanpa mengakses website. Penelitian ini menerapkan metode kompresi Huffman untuk meningkatkan jumlah informasi yang dapat ditampung dalam kode QR. Kompresi Huffman merupakan algoritme kompresi lossless yang cocok digunakan pada data teks. Tahapan yang dilakukan pada penelitian ini terdiri atas analisis kebutuhan, perancangan, implementasi dan pengujian. Sistem yang dibangun terdiri atas dua aplikasi, yaitu aplikasi generator dan aplikasi pemindai kode QR. Aplikasi generator berfungsi menjalankan kompresi dan menghasilkan kode QR. Aplikasi pemindai kode QR berfungsi memindai kode QR dan melakukan dekompresi serta menampilkan informasi ke layar smartphone. Hasil penelitian menunjukkan kedua aplikasi dapat berfungsi dengan baik. Efisiensi hasil kompresi pada 30 data teks yang diuji mencapai rata-rata 56,6%. Hal ini menjadikan kode QR dapat menampung data informasi rata-rata dua kali lipat data teks tanpa kompresi.

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“…Moreover, in computer science [ 12 , 13 ], the authors showed that cataloging network traffic and a computer virus is possible. It also has applications in computer security since, for example, one can break multi-party computations based on [ 14 ]. In biology, NCD was used to classify mitochondrial genome phylogeny [ 15 ].…”

Section: Introductionmentioning

confidence: 99%

Zgli: A Pipeline for Clustering by Compression with Application to Patient Stratification in Spondyloarthritis

Azevedo

Rodrigues

Canhão

et al. 2023

Sensors

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The normalized compression distance (NCD) is a similarity measure between a pair of finite objects based on compression. Clustering methods usually use distances (e.g., Euclidean distance, Manhattan distance) to measure the similarity between objects. The NCD is yet another distance with particular characteristics that can be used to build the starting distance matrix for methods such as hierarchical clustering or K-medoids. In this work, we propose Zgli, a novel Python module that enables the user to compute the NCD between files inside a given folder. Inspired by the CompLearn Linux command line tool, this module iterates on it by providing new text file compressors, a new compression-by-column option for tabular data, such as CSV files, and an encoder for small files made up of categorical data. Our results demonstrate that compression by column can yield better results than previous methods in the literature when clustering tabular data. Additionally, the categorical encoder shows that it can augment categorical data, allowing the use of the NCD for new data types. One of the advantages is that using this new feature does not require knowledge or context of the data. Furthermore, the fact that the new proposed module is written in Python, one of the most popular programming languages for machine learning, potentiates its use by developers to tackle problems with a new approach based on compression. This pipeline was tested in clinical data and proved a promising computational strategy by providing patient stratification via clusters aiding in precision medicine.

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Breaking MPC implementations through compression

Cited by 5 publications

References 18 publications

A Survey on Using Kolmogorov Complexity in Cybersecurity

A Survey on Using Kolmogorov Complexity in Cybersecurity

Penerapan Metode Kompresi Huffman untuk Meningkatkan Jumlah Data Informasi pada Kode Quick Response

Zgli: A Pipeline for Clustering by Compression with Application to Patient Stratification in Spondyloarthritis

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