Abstract— The present study deals with Transparent Data Encryption which is a technology used to solve the problems of security of data. Transparent Data Encryption means encryptingdatabases on hard disk and on any backup media. Present day global business environment presents numerous security threats and compliance challenges. To protect against data thefts andfrauds we require security solutions that are transparent by design. Transparent Data Encryption provides transparent, standards-based security that protects data on the network, on disk and on backup media. It is easy and effective protection ofstored data by transparently encrypting data. Transparent Data Encryption can be used to provide high levels of security to columns, table and tablespace that is database files stored onhard drives or floppy disks or CD’s, and other information that requires protection. It is the technology used by Microsoft SQL Server 2008 to encrypt database contents. The term encryptionmeans the piece of information encoded in such a way that it can only be decoded read and understood by people for whom the information is intended. The study deals with ways to createMaster Key, creation of certificate protected by the master key, creation of database master key and protection by the certificate and ways to set the database to use encryption in Microsoft SQLServer 2008.

The information in this publication identifies and describes approved cryptographic algorithms and appropriate methods of use to protect the confidentiality of PROTECTED A and PROTECTED B information and the integrity of information to the medium injury level as defined in CSE’s ITSG-33 IT Security Risk Management: A Lifecycle Approach [6].

AbstractPresent day global business environment presents numerous security threats and compliance challenges. To protect against data thefts and frauds we require security solutions that aretransparent by design. The present study deals with Transparent Data Encryption which is a technology used to solve the problems of security of data. Transparent Data Encryption means encryptingdatabases on hard disk and on any backup media. Transparent Data Encryption provides transparent, standards-based security that protects data on the network, on disk and on backup media.It is easy and effective protection of stored data by transparently encrypting data. Transparent Data Encryption can be used to provide high levels of security to columns, table and tablespacethat is database files stored on hard drives or floppy disks or CD’s, and other information that requires protection. It is the technology used by Microsoft SQL Server 2008, Oracle 10g and 11g to encrypt database contents. The term encryption means thepiece of information encoded in such a way that it can only be decoded read and understood by people for whom the information is intended. The study deals with ways to create Master Key, creation of certificate protected by the master key, creation ofdatabase master key and protection by the certificate and ways to set the database to use encryption in Microsoft SQL Server 2008,Oracle 10g and 11g.

Datasets collection for ECC (C25519) side-channel traces, as part of REASSURE H2020 731591 project.

The set “REASSURE_c25519_arithm_6k + PatternExtract From 5997 traces + StaticAlign.trs” contains electromagnetic traces coming from 5997 executions of Curve25519 $\mu$NaCl Montgomery Ladder scalar multiplication:
http://munacl.cryptojedi.org/curve25519-cortexm0.shtml
running on the Pi\~{n}ata target:
https://www.riscure.com/product/pinata-training-target/
which is a 32-bit STM32F4 microcontroller with an ARM-based architecture, running at the clock frequency of 168 MHz.

The implementation employs arithmetic-based conditional swap and is additionally protected with projective coordinate re-randomization and scalar randomization.
Each trace from the dataset represent a single iteration of the Montgomery Ladder scalar multiplication that is cut from the whole execution trace; such trace is labeled with the corresponding cswap condition bit.
Observe that a full scalar can be trivially recovered from the cswap condition bits used in the 255 Montgomery Ladder iterations.
Furthermore, all these cut traces (5997*255=1529235) are aligned to exploit the leakage efficiently.
Details about the implementation and how the traces are aligned are in:
https://eprint.iacr.org/2016/923.pdf

The set “REASSURE_c25519_arithm_6k + PatternExtract From 100 traces + StaticAlign.trs” contains a part of the 5997 set, but limited to the first 100 full traces.

The set “REASSURE_c25519_arithm_6k_100 full traces.trs” contains the full 100 traces (before division).

Each traces is in the TRS format that is described under the following links:
https://github.com/Riscure/python-trsfile
https://github.com/Riscure/java-trsfile
https://github.com/Riscure/Jlsca

Moreover, note that each trs file include a short description inside the file itself.

Cyber security guidance

Software and hardware side-channel analysis databases for attack on Ascon AEAD

This repository contains datasets collected for side-channel analysis attack on Ascon AEAD.

The repository is divided into two folders, one for software side-channel analysis and one for hardware side-channel analysis:

- cw.zip

- ascon_collect.ipynb : Jupyter Notebook used to collect the traces for the unprotected Ascon implementation

- ascon_protected_collect.ipynb : Jupyter Notebook used to collect the traces for the protected Ascon implementation

- simpleserial-ascon : Ascon firmwares used to collect the traces

- hw.zip

- ascon_g_protected

- test_ascon.py : Script to test the protected Ascon implementation

- collect_lecroy.py : Script to collect traces for unprotected Ascon implementation with Lecroy oscilloscope

- rtl_src : RTL source files for the protected Ascon implementation

- ascon_g_unprotected

- test_ascon.py : Script to test the unprotected Ascon implementation

- collect_lecroy.py : Script to collect traces for unprotected Ascon implementation with Lecroy oscilloscope

- rtl_src : RTL source files for the unprotected Ascon implementation

- helpers.zip

- ASCON.py : Python implementation of Ascon

- SASEBO.py : Helper functions to communicate with the SAKURA-G board

- lecroy3.py : Helper functions for Lecroy oscilloscope

- ascon_helper.py : Helper functions for Ascon

- convert_trs_to_h5.py : Script to convert Trsfile traceset to HDF5 database

ascon_cw_protected.h5 : Side-channel database for software protected Ascon implementation

ascon_cw_unprotected.h5 : Side-channel database for software unprotected Ascon implementation

ascon_hw_protected.h5 : Side-channel database for hardware protected Ascon implementation

ascon_hw_unprotected.h5 : Side-channel database for hardware unprotected Ascon implementation

ascon_cw_protected.trs : Traces for software protected Ascon implementation

ascon_cw_unprotected.trs : Traces for software unprotected Ascon implementation

ascon_hw_protected.trs : Traces for hardware protected Ascon implementation

ascon_hw_unprotected.trs : Traces for hardware unprotected Ascon implementation

Ascon authenticated encryption attack on a Chipwhisperer STM32F4

The dataset was used for side-channel attack on Ascon initialization phase attack of the authenticated encryption mode on a ChipWhisperer STM32F4 target board.

The power traces are collected with the ChipWhisperer-Lite oscilloscope at a sampling rate of 4x the target clock frequency, and captures the first call of the round function of the Chi function of Ascon permutation.

The code used to collect the traces is also available in this repository, and the trace collection can be replicated with a ChipWhisperer-Lite and a STM32F4 target board.

Ascon authenticated encryption attack on a SAKURA-G FPGA

The hardware designs of the unprotected and protected Ascon implementations are available in the hw folder.

Both implementations are written in VHDL/Verilog and can be synthesized for Spartan6 (XC6SLX75) with Xilinx ISE.

Traces are collected with a Lecroy WaveRunner 610Zi oscilloscope at a sampling rate of 500 MS/s.

Databases description:

Database | Ntraces | Traces (samples) | Label* (bytes) | Metadata (bytes)

| Nf | Nr | | | Key | Nonce | Plaintext | Associated data | Ciphertext | Tag

ascon_cw_unprotected.h5 | 100,000 | 100,000 | 772 | 64 | 16 | 16 | 4 | 4 | 4 | 16

ascon_cw_protected.h5 | 500,000 | 500,000 | 1408 | 64 | 32 | 32 | 16 | 16 | 16 | 32

ascon_hw_unprotected.h5 | 100,000 | 100,000 | 6,000 | 64 | 16 | 16 | 4 | 4 | 4 | 16

ascon_hw_protected.h5 | 500,000 | 500,000 | 10,000 | 64 | 32 | 32 | 8 | 8 | 8 | 32

*Label computed with the intermediate_value leakage model described in ascon_helper.py

The commercial encryption software market is experiencing robust growth, projected to reach a value of $10.54 billion in 2025 and maintain a Compound Annual Growth Rate (CAGR) of 12% from 2025 to 2033. This expansion is fueled by several key factors. The increasing prevalence of cyber threats and data breaches across various sectors, including finance, government, and healthcare, is driving the demand for robust encryption solutions to safeguard sensitive information. Furthermore, the rise of cloud computing and the increasing adoption of remote work models necessitate enhanced security measures, boosting the market for encryption software that protects data both in transit and at rest. The market is segmented by encryption type (disk, file/folder, database, communication, cloud) and application (financial, electric power, government, IT, transport, education, and others), offering diverse solutions tailored to specific organizational needs. Competition is fierce, with established players like Dell, IBM, McAfee, and Microsoft alongside specialized security firms such as Eset, Gemalto, and Thales E-Security vying for market share. Geographic growth is expected across all regions, with North America and Europe likely to maintain significant market dominance due to early adoption and stringent data protection regulations. However, the Asia-Pacific region is anticipated to show rapid growth in the coming years, driven by increasing digitalization and infrastructure development in countries like China and India. The market's sustained growth trajectory is expected to continue throughout the forecast period. This is underpinned by the growing awareness of cybersecurity risks and the increasing regulatory pressures mandating data encryption. Innovation in encryption technologies, including advancements in quantum-resistant cryptography, will further propel market growth. While certain restraining factors, such as the complexity of implementing encryption solutions and the potential cost involved, exist, the overall market outlook remains positive due to the outweighing benefits of data protection in an increasingly interconnected world. The diverse range of solutions available, catering to specific needs and budgets, ensures widespread market penetration across various industries and geographic locations.

Data Encryption Market Overview The global data encryption market is projected to register significant growth, with a market size of USD 14.5 billion in 2025 and a CAGR of 16% over the forecast period of 2025-2033. The increasing adoption of cloud computing and digital transformation initiatives are driving the demand for data encryption solutions to protect sensitive data from cyber threats. Additionally, industry regulations, such as GDPR and CCPA, are mandating organizations to implement data encryption measures, further fueling market growth. Market Drivers, Restraints, and Trends Key market drivers include rising cybersecurity threats, increasing data breaches, and the growing need for data privacy. The increasing adoption of IoT and mobile computing is also contributing to the need for data encryption. However, the high cost of implementation and the lack of skilled professionals can pose challenges to market growth. Notable market trends include the emergence of advanced encryption algorithms, such as quantum-safe cryptography, and the integration of encryption with AI and machine learning technologies. Regional factors, such as government regulations and technology adoption rates, also influence the market's growth trajectory. Recent developments include: On Apr. 11, 2023, Menlo Security, a leading provider of browser security solutions, published the results of the 10th Annual Cyberthreat Defense Report (CDR) by the CyberEdge Group. The report, partially sponsored by Menlo Security, highlights the augmenting importance of browser isolation technologies to combat ransomware and other malicious threats., The research revealed that most ransomware attacks include threats beyond data encryption. According to the report, around 51% of respondents confirmed that they have been using at least one type of browser or Internet isolation to protect their organizational data, while another 40% are about to deploy data encryption technology. Furthermore, around 33% of respondents noted that browser isolation is a key cybersecurity strategy to protect against sophisticated attacks, including ransomware, phishing, and zero-day attacks., On Feb.14, 2023, EnterpriseDB, a relational database provider, announced the addition of Transparent Data Encryption (TDE) based on open-source PostgreSQL to its databases. The new TDE feature will be shipped along with the firm's enterprise version of its database. TDE is a method of encrypting database files to ensure data security while at rest and in motion., Adding that most enterprises use TDE for compliance issues helps ensure data encryption on the hard drive and files on a backup. Before the development of built-in TDE, enterprises relied on either full-disk encryption or stackable cryptographic file system encryption., On Jan.25, 2023, Researchers from the Tokyo University of Science, Japan, announced the development of a faster and cheaper method for handling encrypted data while improving security. The new data encryption method developed by Japanese researchers combines the best of homomorphic encryption and secret sharing to handle encrypted data., Homomorphic encryption and secret sharing are key methods to compute sensitive data while preserving privacy. Homomorphic encryption is computationally intensive and involves performing computational data encryption on a single server, while secret sharing is fast and computationally efficient., In this method, the encrypted data/secret input is divided and distributed across multiple servers, each performing a computation, such as multiplication, on its data. The results of the computations are then used to reconstruct the original data., September 2022: Convergence Technology Solutions Corp., a supplier of software-enabled IT and cloud solutions, declared that it has obtained certification in Canada to sell and deploy IBM zsystems and LinuxONE., November 2019: Penta Security Systems announced that it has been selected as a finalist for the 2020 SC Magazine Awards, which are given by SC Media and celebrated in the United States. As a result, MyDiamo from Penta Security has been named the Best Database Security Solution of 2020. Additionally, this will result in the expansion of common-level encryption and improve the open-source DBMS installation procedure.. Potential restraints include: ISSUE REGARDING SECURITY AND DATA BREACH 44, HIGH IMPLEMENTATION COSTS AND COMPLEXITY 45; ISSUE WITH RESPECT TO DATA CONSISTENCY AND INTEROPERABILITY ACROSS DIFFERENT EDGE PLATFORMS 45.

Preventing unauthorized access to sensitive data has always been one of the main concerns in the field of information security. Accordingly, various solutions have been proposed to meet this requirement, among which encryption can be considered as one of the first and most effective solutions. The continuous increase in the computational power of computers and the rapid development of artificial intelligence techniques have made many previous encryption solutions not secure enough to protect data. Therefore, there is always a need to provide new and more efficient strategies for encrypting information. In this article, a two-way approach for information encryption based on chaos theory is presented. To this end, a new chaos model is first proposed. This model, in addition to having a larger key space and high sensitivity to slight key changes, can demonstrate a higher level of chaotic behavior compared to previous models. In the proposed method, first, the input is converted to a vector of bytes and first diffusion is applied on it. Then, the permutation order of chaotic sequence is used for diffusing bytes of data. In the next step, the chaotic sequence is used for applying second diffusion on confused data. Finally, to further reduce the data correlation, an iterative reversible rule-based model is used to apply final diffusion on data. The performance of the proposed method in encrypting image, text, and audio data was evaluated. The analysis of the test results showed that the proposed encryption strategy can demonstrate a pattern close to a random state by reducing data correlation at least 28.57% compared to previous works. Also, the data encrypted by proposed method, show at least 14.15% and 1.79% increment in terms of MSE and BER, respectively. In addition, key sensitivity of 10−28 and average entropy of 7.9993 in the proposed model, indicate its high resistance to brute-force, statistical, plaintext and differential attacks.

Global Cloud Database and DBaaS Market was valued at $16.97 Billion in 2023, and is projected to reach $USD 80.93 Billion by 2032, at a CAGR of 16.9%.

Comparison of the key length between RSA and ECC on the same security level.

ASCII value.

Result for dynamic synchronization errors for the multi-time delay, multi time-varying delay and without delay.