PIITracker

Arefi, Meisam Navaki; Alexander, Geoffrey; Crandall, Jedidiah R.

doi:10.1145/3193111.3193114

Cited by 3 publications

(1 citation statement)

References 24 publications

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“…Another tool [15,16] focused on hidden or unintentionally inserted identity attributes from identity providers of federated identity management systems. Arefi et al [17] proposed PIITracker which is capable of detecting if there is a transfer of a set identity attributes over the network.…”

Section: Identity Attributes In Generalmentioning

confidence: 99%

Personalized Privacy Assistant: Identity Construction and Privacy in the Internet of Things

Chang

Barber

2023

Entropy

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Over time, the many different ways in which we collect and use data have become more complex as we communicate and interact with an ever-increasing variety of modern technologies. Although people often say they care about their privacy, they do not have a deep understanding of what devices around them are collecting their identity information, what identity information is being collected, and how that collected data will affect them. This research is dedicated to developing a personalized privacy assistant to help users regain control, understand their own identity management, and process and simplify the large amount of information from the Internet of Things (IoT). This research constructs an empirical study to obtain the comprehensive list of identity attributes that are being collected by IoT devices. We build a statistical model to simulate the identity theft and to help calculate the privacy risk score based on the identity attributes collected by IoT devices. We discuss how well each feature of our Personal Privacy Assistant (PPA) works and compare the PPA and related work to a list of fundamental features for privacy protection.

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Section: Identity Attributes In Generalmentioning

confidence: 99%

Personalized Privacy Assistant: Identity Construction and Privacy in the Internet of Things

Chang

Barber

2023

Entropy

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Mitigating Threats in a Corporate Network with a Taintcheck-Enabled Honeypot

John

Albert

Okokpujie

et al. 2019

Lecture Notes in Electrical Engineering

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Conventional network security tools such as Intrusion Detection Systems (IDS), Intrusion Prevention Systems (IPS), anti-virus, antispyware and anti-malware integrated with firewalls generate a lot of false positives that make computer network system administration cumbersome. This paper proposes a novel mechanism comprising of taintcheck for dynamic analysis of buffer overflow attack using synthetic exploit and hybrid honeypot for scanning, detecting, identifying attackers and signature generation. In this framework, Noah's attack detection is used as a template. Upon testing, the practicality of the proposed framework was found to be more effective than other conventional network security tools as it effectively and comprehensively mitigates against threats and reported zero-day attacks with fewer false positives.

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Use & Abuse of Personal Information, Part II: Robust Generation of Fake IDs for Privacy Experimentation

Kolenbrander,

Husmann,

Henshaw

et al. 2024

JCP

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When personal information is shared across the Internet, we have limited confidence that the designated second party will safeguard it as we would prefer. Privacy policies offer insight into the best practices and intent of the organization, yet most are written so loosely that sharing with undefined third parties is to be anticipated. Tracking these sharing behaviors and identifying the source of unwanted content is exceedingly difficult when personal information is shared with multiple such second parties. This paper formulates a model for realistic fake identities, constructs a robust fake identity generator, and outlines management methods targeted towards online transactions (email, phone, text) that pass both cursory machine and human examination for use in personal privacy experimentation. This fake ID generator, combined with a custom account signup engine, are the core front-end components of our larger Use and Abuse of Personal Information system that performs one-time transactions that, similar to a cryptographic one-time pad, ensure that we can attribute the sharing back to the single one-time transaction and/or specific second party. The flexibility and richness of the fake IDs also serve as a foundational set of control variables for a wide range of social science research questions revolving around personal information. Collectively, these fake identity models address multiple inter-disciplinary areas of common interest and serve as a foundation for eliciting and quantifying personal information-sharing behaviors.

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PIITracker

Cited by 3 publications

References 24 publications

Personalized Privacy Assistant: Identity Construction and Privacy in the Internet of Things

Personalized Privacy Assistant: Identity Construction and Privacy in the Internet of Things

Mitigating Threats in a Corporate Network with a Taintcheck-Enabled Honeypot

Use & Abuse of Personal Information, Part II: Robust Generation of Fake IDs for Privacy Experimentation

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