data sanitization
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2022 ◽  
Vol 12 (1) ◽  
Author(s):  
Seong-Joo Han ◽  
Joon-Kyu Han ◽  
Gyeong-Jun Yun ◽  
Mun-Woo Lee ◽  
Ji-Man Yu ◽  
...  

AbstractAlthough SRAM is a well-established type of volatile memory, data remanence has been observed at low temperature even for a power-off state, and thus it is vulnerable to a physical cold boot attack. To address this, an ultra-fast data sanitization method within 5 ns is demonstrated with physics-based simulations for avoidance of the cold boot attack to SRAM. Back-bias, which can control device parameters of CMOS, such as threshold voltage and leakage current, was utilized for the ultra-fast data sanitization. It is applicable to temporary erasing with data recoverability against a low-level attack as well as permanent erasing with data irrecoverability against a high-level attack.


2021 ◽  
Author(s):  
Pang Wei Koh ◽  
Jacob Steinhardt ◽  
Percy Liang
Keyword(s):  

Symmetry ◽  
2021 ◽  
Vol 13 (10) ◽  
pp. 1912
Author(s):  
Md. Mokhlesur Rahman ◽  
Ravie Chandren Muniyandi ◽  
Shahnorbanun Sahran ◽  
Suziyani Mohamed

Interrupting, altering, or stealing autism-related sensitive data by cyber attackers is a lucrative business which is increasing in prevalence on a daily basis. Enhancing the security and privacy of autism data while adhering to the symmetric encryption concept is a critical challenge in the field of information security. To identify autism perfectly and for its data protection, the security and privacy of these data are pivotal concerns when transmitting information over the Internet. Consequently, researchers utilize software or hardware disk encryption, data backup, Data Encryption Standard (DES), TripleDES, Advanced Encryption Standard (AES), Rivest Cipher 4 (RC4), and others. Moreover, several studies employ k-anonymity and query to address security concerns, but these necessitate a significant amount of time and computational resources. Here, we proposed the sanitization approach for autism data security and privacy. During this sanitization process, sensitive data are concealed, which avoids the leakage of sensitive information. An optimal key was generated based on our improved meta-heuristic algorithmic framework called Enhanced Combined PSO-GWO (Particle Swarm Optimization-Grey Wolf Optimization) framework. Finally, we compared our simulation results with traditional algorithms, and it achieved increased output effectively. Therefore, this finding shows that data security and privacy in autism can be improved by enhancing an optimal key used in the data sanitization process to prevent unauthorized access to and misuse of data.


2021 ◽  
Vol 189 ◽  
pp. 107914
Author(s):  
Usman Ahmed ◽  
Gautam Srivastava ◽  
Jerry Chun-Wei Lin

2021 ◽  
Vol 24 (3) ◽  
pp. 1-36
Author(s):  
Meisam Mohammady ◽  
Momen Oqaily ◽  
Lingyu Wang ◽  
Yuan Hong ◽  
Habib Louafi ◽  
...  

As network security monitoring grows more sophisticated, there is an increasing need for outsourcing such tasks to third-party analysts. However, organizations are usually reluctant to share their network traces due to privacy concerns over sensitive information, e.g., network and system configuration, which may potentially be exploited for attacks. In cases where data owners are convinced to share their network traces, the data are typically subjected to certain anonymization techniques, e.g., CryptoPAn, which replaces real IP addresses with prefix-preserving pseudonyms. However, most such techniques either are vulnerable to adversaries with prior knowledge about some network flows in the traces or require heavy data sanitization or perturbation, which may result in a significant loss of data utility. In this article, we aim to preserve both privacy and utility through shifting the trade-off from between privacy and utility to between privacy and computational cost. The key idea is for the analysts to generate and analyze multiple anonymized views of the original network traces: Those views are designed to be sufficiently indistinguishable even to adversaries armed with prior knowledge, which preserves the privacy, whereas one of the views will yield true analysis results privately retrieved by the data owner, which preserves the utility. We formally analyze the privacy of our solution and experimentally evaluate it using real network traces provided by a major ISP. The experimental results show that our approach can significantly reduce the level of information leakage (e.g., less than 1% of the information leaked by CryptoPAn) with comparable utility.


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