The Experts below are selected from a list of 11184 Experts worldwide ranked by ideXlab platform
Toreini Ehsan - One of the best experts on this subject based on the ideXlab platform.
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How Can and Would People Protect From Online Tracking?
'Walter de Gruyter GmbH', 2022Co-Authors: Mehrnezhad Maryam, Coopamootoo Kovila, Toreini EhsanAbstract:Online Tracking is complex and users find itchallenging to protect themselves from it. While the aca-demic community has extensively studied systems andusers for Tracking practices, the link between the dataProtection regulations, websites’ practices of presentingprivacy-enhancing technologies (PETs), and how userslearn about PETs and practice them is not clear. Thispaper takes a multidimensional approach to find such alink. We conduct a study to evaluate the 100 top EUwebsites, where we find that information about PETsis provided far beyond the cookie notice. We also findthat opting-out from privacy settings is not as easy asopting-in and becomes even more difficult (if not impos-sible) when the user decides to opt-out of previously ac-cepted privacy settings. In addition, we conduct an on-line survey with 614 participants across three countries(UK, France, Germany) to gain a broad understand-ing of users’ Tracking Protection practices. We find thatusers mostly learn about PETs for Tracking Protectionvia their own research or with the help of family andfriends. We find a disparity between what websites offeras Tracking Protection and the ways individuals reportto do so. Observing such a disparity sheds light on whycurrent policies and practices are ineffective in support-ing the use of PETs by users
Mehrnezhad Maryam - One of the best experts on this subject based on the ideXlab platform.
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How Can and Would People Protect From Online Tracking?
'Walter de Gruyter GmbH', 2022Co-Authors: Mehrnezhad Maryam, Coopamootoo Kovila, Toreini EhsanAbstract:Online Tracking is complex and users find itchallenging to protect themselves from it. While the aca-demic community has extensively studied systems andusers for Tracking practices, the link between the dataProtection regulations, websites’ practices of presentingprivacy-enhancing technologies (PETs), and how userslearn about PETs and practice them is not clear. Thispaper takes a multidimensional approach to find such alink. We conduct a study to evaluate the 100 top EUwebsites, where we find that information about PETsis provided far beyond the cookie notice. We also findthat opting-out from privacy settings is not as easy asopting-in and becomes even more difficult (if not impos-sible) when the user decides to opt-out of previously ac-cepted privacy settings. In addition, we conduct an on-line survey with 614 participants across three countries(UK, France, Germany) to gain a broad understand-ing of users’ Tracking Protection practices. We find thatusers mostly learn about PETs for Tracking Protectionvia their own research or with the help of family andfriends. We find a disparity between what websites offeras Tracking Protection and the ways individuals reportto do so. Observing such a disparity sheds light on whycurrent policies and practices are ineffective in support-ing the use of PETs by users
Coopamootoo Kovila - One of the best experts on this subject based on the ideXlab platform.
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How Can and Would People Protect From Online Tracking?
'Walter de Gruyter GmbH', 2022Co-Authors: Mehrnezhad Maryam, Coopamootoo Kovila, Toreini EhsanAbstract:Online Tracking is complex and users find itchallenging to protect themselves from it. While the aca-demic community has extensively studied systems andusers for Tracking practices, the link between the dataProtection regulations, websites’ practices of presentingprivacy-enhancing technologies (PETs), and how userslearn about PETs and practice them is not clear. Thispaper takes a multidimensional approach to find such alink. We conduct a study to evaluate the 100 top EUwebsites, where we find that information about PETsis provided far beyond the cookie notice. We also findthat opting-out from privacy settings is not as easy asopting-in and becomes even more difficult (if not impos-sible) when the user decides to opt-out of previously ac-cepted privacy settings. In addition, we conduct an on-line survey with 614 participants across three countries(UK, France, Germany) to gain a broad understand-ing of users’ Tracking Protection practices. We find thatusers mostly learn about PETs for Tracking Protectionvia their own research or with the help of family andfriends. We find a disparity between what websites offeras Tracking Protection and the ways individuals reportto do so. Observing such a disparity sheds light on whycurrent policies and practices are ineffective in support-ing the use of PETs by users
Beresford, Alastair R - One of the best experts on this subject based on the ideXlab platform.
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Factory Calibration Fingerprinting of Sensors
'Organisation for Economic Co-Operation and Development (OECD)', 2021Co-Authors: Zhang Jiexin, Beresford, Alastair RAbstract:Device fingerprinting aims to generate a distinctive signature, or fingerprint, that uniquely identifies individual computing devices. Fingerprints may be a privacy concern since apps and websites can use them to track user activity online. To protect user privacy, both Android and iOS have included a variety of measures to prevent such Tracking. In this paper we present a new type of fingerprinting, factory calibration fingerprinting, that bypasses existing Tracking Protection. Our attack recovers embedded per-device factory calibration data from the accelerometer, gyroscope, and magnetometer sensors that are pervasive in modern smartphones by careful analysis of the sensor output alone. We discuss the factory calibration behaviour of each sensor and show that the calibration fingerprint is fast to generate, does not change over time or after a factory reset, and can be used to track users across apps and websites without any special permission from the user. We find the calibration fingerprint is very likely to be globally unique for iOS devices, with an estimated 67 bits of entropy for the iPhone 6S. In addition, we have analysed 146 Android device models from 11 vendors and found the attack also works on recent Google Pixel devices. For Pixel 4/4 XL, we estimate the calibration fingerprint provides about 57 bits of entropy. Following our disclosures, Apple deployed a mitigation in iOS 12.2 and Google in Android 11. We analyse Apple's fix and show that the mitigation is imperfect although it is likely to be sufficient in most threat models.China Scholarship Counci
Zhang Jiexin - One of the best experts on this subject based on the ideXlab platform.
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Factory Calibration Fingerprinting of Sensors
'Organisation for Economic Co-Operation and Development (OECD)', 2021Co-Authors: Zhang Jiexin, Beresford, Alastair RAbstract:Device fingerprinting aims to generate a distinctive signature, or fingerprint, that uniquely identifies individual computing devices. Fingerprints may be a privacy concern since apps and websites can use them to track user activity online. To protect user privacy, both Android and iOS have included a variety of measures to prevent such Tracking. In this paper we present a new type of fingerprinting, factory calibration fingerprinting, that bypasses existing Tracking Protection. Our attack recovers embedded per-device factory calibration data from the accelerometer, gyroscope, and magnetometer sensors that are pervasive in modern smartphones by careful analysis of the sensor output alone. We discuss the factory calibration behaviour of each sensor and show that the calibration fingerprint is fast to generate, does not change over time or after a factory reset, and can be used to track users across apps and websites without any special permission from the user. We find the calibration fingerprint is very likely to be globally unique for iOS devices, with an estimated 67 bits of entropy for the iPhone 6S. In addition, we have analysed 146 Android device models from 11 vendors and found the attack also works on recent Google Pixel devices. For Pixel 4/4 XL, we estimate the calibration fingerprint provides about 57 bits of entropy. Following our disclosures, Apple deployed a mitigation in iOS 12.2 and Google in Android 11. We analyse Apple's fix and show that the mitigation is imperfect although it is likely to be sufficient in most threat models.China Scholarship Counci