LH
Leon J. Helsloot
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4 records found
1
Badass
Preserving privacy in behavioural advertising with applied secret sharing
Online advertising is a multi-billion dollar industry, forming the primary source of income for many publishers offering free web content. Serving advertisements tailored to users’ interests greatly improves the effectiveness of advertisements, and is believed to be beneficial to publishers and users alike. The privacy of users, however, is threatened by the widespread collection of data that is required for behavioural advertising. In this paper, we present BAdASS, a novel privacy-preserving protocol for Online Behavioural Advertising that achieves significant performance improvements over the state-of-the-art without disclosing any information about user interests to any party. BAdASS ensures user privacy by processing data within the secret-shared domain, using the heavily fragmented shape of the online advertising landscape to its advantage and combining efficient secret-sharing techniques with a machine learning method commonly encountered in existing advertising systems. Our protocol serves advertisements within a fraction of a second, based on highly detailed user profiles and widely used machine learning methods.
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Online advertising is a multi-billion dollar industry, forming the primary source of income for many publishers offering free web content. Serving advertisements tailored to users’ interests greatly improves the effectiveness of advertisements, and is believed to be beneficial to publishers and users alike. The privacy of users, however, is threatened by the widespread collection of data that is required for behavioural advertising. In this paper, we present BAdASS, a novel privacy-preserving protocol for Online Behavioural Advertising that achieves significant performance improvements over the state-of-the-art without disclosing any information about user interests to any party. BAdASS ensures user privacy by processing data within the secret-shared domain, using the heavily fragmented shape of the online advertising landscape to its advantage and combining efficient secret-sharing techniques with a machine learning method commonly encountered in existing advertising systems. Our protocol serves advertisements within a fraction of a second, based on highly detailed user profiles and widely used machine learning methods.
BAdASS
Preserving privacy in behavioural advertising with applied secret sharing
Online advertising forms the primary source of income for many publishers offering free web content by serving advertisements tailored to users’ interests. The privacy of users, however, is threatened by the widespread collection of data that is required for behavioural advertising. In this paper, we present BAdASS, a novel privacy-preserving protocol for Online Behavioural Advertising that achieves significant performance improvements over the state-of-the-art without disclosing any information about user interests to any party. BAdASS ensures user privacy by combining efficient secret-sharing techniques with a machine learning method commonly encountered in existing systems. Our protocol serves advertisements within a fraction of a second, based on highly detailed user profiles and widely used machine learning methods.
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Online advertising forms the primary source of income for many publishers offering free web content by serving advertisements tailored to users’ interests. The privacy of users, however, is threatened by the widespread collection of data that is required for behavioural advertising. In this paper, we present BAdASS, a novel privacy-preserving protocol for Online Behavioural Advertising that achieves significant performance improvements over the state-of-the-art without disclosing any information about user interests to any party. BAdASS ensures user privacy by combining efficient secret-sharing techniques with a machine learning method commonly encountered in existing systems. Our protocol serves advertisements within a fraction of a second, based on highly detailed user profiles and widely used machine learning methods.
AHEad
Privacy-preserving Online Behavioural Advertising using Homomorphic Encryption
Online advertising is a rapidly growing industry, forming the primary source of income for many publishers that offer free web content. The practice of serving advertisements based on individuals' interests greatly improves the expected effectiveness of advertisements, and is believed to be beneficial to publishers and users alike. However, the widespread data collection required for such behavioural advertising sparks concerns over user privacy. In this paper, we present AHEad, a privacy-preserving protocol for Online Behavioural Advertising that ensures user privacy by processing data in encrypted form. AHEad combines homomorphic encryption with a machine learning method commonly encountered in existing advertising systems. Advertisements are served based on detailed user profiles, while achieving performance linear in the size of user profiles. To the best of our knowledge, AHEad is the first protocol that preserves user privacy in behavioural advertising while allowing the use of detailed user profiles and machine learning methods.
...
Online advertising is a rapidly growing industry, forming the primary source of income for many publishers that offer free web content. The practice of serving advertisements based on individuals' interests greatly improves the expected effectiveness of advertisements, and is believed to be beneficial to publishers and users alike. However, the widespread data collection required for such behavioural advertising sparks concerns over user privacy. In this paper, we present AHEad, a privacy-preserving protocol for Online Behavioural Advertising that ensures user privacy by processing data in encrypted form. AHEad combines homomorphic encryption with a machine learning method commonly encountered in existing advertising systems. Advertisements are served based on detailed user profiles, while achieving performance linear in the size of user profiles. To the best of our knowledge, AHEad is the first protocol that preserves user privacy in behavioural advertising while allowing the use of detailed user profiles and machine learning methods.
Online Behavioural Advertising (OBA), the practice of showing advertisements based on a person’s web browsing behaviour, has become a vital component of the ad-supported web. The tracking of users’ browsing behaviour that is needed for OBA, however, raises privacy concerns. We give an overview of the OBA landscape, and describe which user information is collected, which techniques are used to perform the collection, and how user information is shared between companies. Moreover, we discuss the privacy concerns that are raised by current OBA practices. After identifying privacy concerns, we describe a range of existing techniques to protect user privacy in online advertising. These techniques are compared based on their feasibility in the current advertising ecosystem, including the potential utility they offer advertising companies and how well they can be integrated with current trends in online behavioural advertising. Finally, we identify open problems in the protection of user privacy in online advertising.
...
Online Behavioural Advertising (OBA), the practice of showing advertisements based on a person’s web browsing behaviour, has become a vital component of the ad-supported web. The tracking of users’ browsing behaviour that is needed for OBA, however, raises privacy concerns. We give an overview of the OBA landscape, and describe which user information is collected, which techniques are used to perform the collection, and how user information is shared between companies. Moreover, we discuss the privacy concerns that are raised by current OBA practices. After identifying privacy concerns, we describe a range of existing techniques to protect user privacy in online advertising. These techniques are compared based on their feasibility in the current advertising ecosystem, including the potential utility they offer advertising companies and how well they can be integrated with current trends in online behavioural advertising. Finally, we identify open problems in the protection of user privacy in online advertising.