Big Data fingerprinting information analytics for sustainability

Kobusińska, Anna; Pawluczuk, Kamil; Brzeziński, Jerzy

doi:10.1016/j.future.2017.12.061

Cited by 8 publications

(6 citation statements)

References 18 publications

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“…In [10] the approach was proven in a larger study and thus it was shown that the digital fingerprint can be effectively used for user identification via statistics. Further studies [4,11,61,62] dealt with extensions of the statistical features and their quality for user recognition. In [4,61] an analysis of the stability of the individual characteristics was also carried out.…”

Section: Related Workmentioning

confidence: 99%

“…Further studies [4,11,61,62] dealt with extensions of the statistical features and their quality for user recognition. In [4,61] an analysis of the stability of the individual characteristics was also carried out. [61,62] examined different browsers and also analyzed security settings that can prevent fingerprinting.…”

Section: Related Workmentioning

confidence: 99%

“…In [4,61] an analysis of the stability of the individual characteristics was also carried out. [61,62] examined different browsers and also analyzed security settings that can prevent fingerprinting. There was also a long term study which dealt with the creation of a unique fingerprint over years [54].…”

Section: Related Workmentioning

confidence: 99%

See 2 more Smart Citations

The Gaze and Mouse Signal as additional Source for User Fingerprints in Browser Applications

Fuhl¹,

Nikolai²,

Kasneci³

2021

Preprint

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

The Gaze and Mouse Signal as additional Source for User Fingerprints in Browser Applications

Fuhl¹,

Nikolai²,

Kasneci³

2021

Preprint

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“…Only in the next study [10], where the research was conducted on a larger number of users, was it shown that fingerprinting can become a unique identifier. Works on browser or device fingerprinting usually contain comprehensive information on specific features comprising the fingerprint [3,10,11,12,14]. The studies also focus on the analysis of stability of particular parameters [3,12], examining different types of browsers [11,12] or applied security features limiting the possibility of obtaining fingerprint features [12].…”

Section: Related Workmentioning

confidence: 99%

“…This happens when updating the browser or operating system version, resizing the screen, installing a new plugin, etc. Once a fingerprint has been obtained, on average, it remains valid for several days [12]. That is why developing a way of comparing fingerprints taking into account the changes occurring in them poses a rather demanding challenge.…”

Section: Introductionmentioning

confidence: 99%

Browser Fingerprint Coding Methods Increasing the Effectiveness of User Identification in the Web Traffic

Gabryel

Grzanek

Hayashi

2020

Journal of Artificial Intelligence and Soft Computing Research

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Web-based browser fingerprint (or device fingerprint) is a tool used to identify and track user activity in web traffic. It is also used to identify computers that are abusing online advertising and also to prevent credit card fraud. A device fingerprint is created by extracting multiple parameter values from a browser API (e.g. operating system type or browser version). The acquired parameter values are then used to create a hash using the hash function. The disadvantage of using this method is too high susceptibility to small, normally occurring changes (e.g. when changing the browser version number or screen resolution). Minor changes in the input values generate a completely different fingerprint hash, making it impossible to find similar ones in the database. On the other hand, omitting these unstable values when creating a hash, significantly limits the ability of the fingerprint to distinguish between devices. This weak point is commonly exploited by fraudsters who knowingly evade this form of protection by deliberately changing the value of device parameters. The paper presents methods that significantly limit this type of activity. New algorithms for coding and comparing fingerprints are presented, in which the values of parameters with low stability and low entropy are especially taken into account. The fingerprint generation methods are based on popular Minhash, the LSH, and autoencoder methods. The effectiveness of coding and comparing each of the presented methods was also examined in comparison with the currently used hash generation method. Authentic data of the devices and browsers of users visiting 186 different websites were collected for the research.

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Application of a Neural Network to Generate the Hash Code for a Device Fingerprint

Gabryel¹,

Kocić²

2021

Artificial Intelligence and Soft Computing

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Big Data fingerprinting information analytics for sustainability

Cited by 8 publications

References 18 publications

The Gaze and Mouse Signal as additional Source for User Fingerprints in Browser Applications

The Gaze and Mouse Signal as additional Source for User Fingerprints in Browser Applications

Browser Fingerprint Coding Methods Increasing the Effectiveness of User Identification in the Web Traffic

Application of a Neural Network to Generate the Hash Code for a Device Fingerprint

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