An SDN/NFV-Enabled Architecture for Detecting Personally Identifiable Information Leaks on Network Traffic

Go, Sharleen Joy Y.; Guinto, Richard F.; Festin, Cedric Angelo M.; Austria, Isabel; Ocampo, Roel; Tan, Wilson M.

doi:10.1109/icufn.2019.8806077

Cited by 5 publications

(5 citation statements)

References 9 publications

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“…Most research works [11,12,13,14] related to PII discovery used string-matching techniques to discover PII and supported data breach detection. For example, AntMonitor [11] and Lumen [12], provided a blacklist of potential PII leaks represented in string.…”

Section: Related Workmentioning

confidence: 99%

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Enabling Efficient Personally Identifiable Information Detection with Automatic Consent Discovery

Fugkeaw

Sanchol

2023

ECTI-CIT Transactions

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Personal data leakage prevention has now become a critical issue for implementing data management and sharing in many industries. Several data privacy regulations such as General Data Protection Regulation (GDPR), Health Insurance Portability and Accountability Act (HIPPA), California Consumer Privacy Act (CCPA), and Thailand's Personal Data Protection Act (PDPA) have been issued to enforce organizations to collect, process, and transfer personally identifiable information (PII) securely. In this paper, we propose a design and development of PII RapidDiscover, an efficient Thai and English PII discovery system featured with automatic consent discovery. At the core of our proposed system, we introduce the PII scanning algorithm based on the Presidio library and a natural language processing (NLP) technique to improve the scan result of PII written in Thai and English. Finally, we conducted the experiments to demonstrate the efficiency of our proposed system.

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Section: Related Workmentioning

confidence: 99%

“…The traffic was routed to analyzed at a centralized remote server. In [14], the authors proposed a PII detector for the virtual network (PIID VNF) to detect plaintext PII strings embedded in HTTP fields. The system investigated the packets containing PII, and then they were marked by the PIID.…”

Section: Related Workmentioning

confidence: 99%

Enabling Efficient Personally Identifiable Information Detection with Automatic Consent Discovery

Fugkeaw

Sanchol

2023

ECTI-CIT Transactions

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“…However, PID disclosure is a risk to be managed appropriately. In fact, in [22] authors are researching at the network traffic level, and they are proposing a Software Defined Networking (SDN) / Network Function Virtualization (NFV)-enabled architecture for improving the efficiency of leak detection systems. This is a similar approach to the one proposed for identifying PII in internet traffic [23].…”

Section: Background Study a Personally Identifiable Informationmentioning

confidence: 99%

Burnable Pseudo-Identity: A Non-Binding Anonymous Identity Method for Ethereum

et al. 2021

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The concept of identity has become one common research topic in security and privacy where the real identity of users must be preserved, usually covered by pseudonym identifiers. With the rise of Blockchain-based systems, identities are becoming even more critical than before, mainly due to the immutability property. In fact, many publicly accessible Blockchain networks like Ethereum rely on pseudonymization as a method for identifying subject actions. Pseudonyms are often employed to maintain anonymity, but true anonymity requires unlinkability. Without this property, any attacker can examine the messages sent by a specific pseudonym and learn new information about the holder of this pseudonym. This use of Blockchain collides with regulations because of the right to be forgotten, and Blockchainbased solutions are ensuring that every data stored within the chain will not be modified. In this paper we define a method and a tool for dealing with digital identities within Blockchain environments that are compliant with regulations. The proposed method provides a way to grant digital pseudo identities unlinked to the real identity. This new method uses the benefits of key derivation systems to ensure a non-binding interaction between users and the information model associated with their identity. The proposed method is demonstated in the Ethereum context and illustrated with a case study.

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“…Over the years, personal data leaking has been widely studied in the existing literature. A recent study by Go et al [14] raises serious concern on the opening and un-intended disclosure of information on social media platforms. The authors suggest an intelligent and flexibly centralized model based on software-defined networking with virtualization to counteract unintended PII opening on the network traffic.…”

Section: Related Workmentioning

confidence: 99%

Personally Identifiable Information (PII) Detection in the Unstructured Large Text Corpus using Natural Language Processing and Unsupervised Learning Technique

Kulkarni¹,

Cauvery²

2021

IJACSA

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Personally Identifiable Information (PII) has gained much attention with the rapid development of technologies and the exploitation of information relating to an individual. The corporates and other organizations store a large amount of information that is primarily disseminated in the form of emails that include personnel information of the user, employee, and customers. The security aspects of PII storage have been ignored, raising serious security concerns onindividual privacy. A significant concern arises about comprehending the responsibilities regarding the uses of PII. However, in real-time scenarios, email data is regarded as unstructured text data, detecting PII from such an unstructured large text corpus is quite challenging. This paper presents an intelligent clustering approach for automatically detecting personally identifiable information (PII) from a large text corpus. The focus of the proposed study is to design a model that receives text content and detects possible PII attributes. Therefore, this paper presents a clustering-based PII Model (C-PPIM) based on NLP and unsupervised learning to address detection of PII in the unstructured large text corpus. NLP is used to perform topic modeling, and Byte mLSTM, a different approach of sequence model, is implemented to address clustering problems in PII detection. The performance analysis of the proposed model is carried out existing hierarchical clustering concerning silhouette and cohesion score. The outcome indicatedthe effectiveness of the proposed system that highlights significant PII attributes, with significant scope in real-time implementation. In contrast, existing techniques are too expensive to function and fit in realtime environments.

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An SDN/NFV-Enabled Architecture for Detecting Personally Identifiable Information Leaks on Network Traffic

Cited by 5 publications

References 9 publications

Enabling Efficient Personally Identifiable Information Detection with Automatic Consent Discovery

Enabling Efficient Personally Identifiable Information Detection with Automatic Consent Discovery

Burnable Pseudo-Identity: A Non-Binding Anonymous Identity Method for Ethereum

Personally Identifiable Information (PII) Detection in the Unstructured Large Text Corpus using Natural Language Processing and Unsupervised Learning Technique

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