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'I didn't buy it for myself' privacy and ecommerce personalization

Published: 30 October 2003 Publication History

Abstract

Ecommerce personalization can help web sites build and retain relationships with customers, but it also raises a number of privacy concerns. This paper outlines the privacy risks associated with personalization and describes a number of approaches to personalization system design that can reduce these risks. This paper also provides an overview of the fair information practice principles and discusses how they may be applied to the design of personalization systems, and introduces privacy laws and self-regulatory guidelines relevant to personalization. Privacy risks can be reduced when personalization system designs allow for pseudonymous interactions, client-side data stores, and task-based personalization. In addition, interfaces that allow users to control the collection and use of their profile information can further ease privacy concerns.

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cover image ACM Conferences
WPES '03: Proceedings of the 2003 ACM workshop on Privacy in the electronic society
October 2003
135 pages
ISBN:1581137761
DOI:10.1145/1005140
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 30 October 2003

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  1. ecommerce
  2. personalization
  3. privacy

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  • (2017)BÖLÜNMÜŞ VERİ-TABANLI GİZLİLİĞİ KORUYAN ORTAK FİLTRELEME SİSTEMLERİNDE GİZLİ VERİNİN ELDE EDİLMESİGazi Üniversitesi Mühendislik-Mimarlık Fakültesi Dergisi10.17341/gazimmfd.30059432:1Online publication date: 23-Mar-2017
  • (2017)Methods of privacy preserving in collaborative filtering2017 International Conference on Computer Science and Engineering (UBMK)10.1109/UBMK.2017.8093386(261-266)Online publication date: Oct-2017
  • (2016)Randomization-based Privacy-preserving Frameworks for Collaborative FilteringProcedia Computer Science10.1016/j.procs.2016.08.09196:C(33-42)Online publication date: 1-Oct-2016
  • (2016)Collaborative privacy framework for minimizing privacy risks in an IPTV social recommender serviceMultimedia Tools and Applications10.1007/s11042-014-2271-075:22(14927-14957)Online publication date: 1-Nov-2016
  • (2016)Behavioural Advertising and the New ‘EU Cookie Law’ as a Victim of Business Resistance and a Lack of Official DeterminationData Protection on the Move10.1007/978-94-017-7376-8_9(213-247)Online publication date: 2016
  • (2015)On the discovery of fake binary ratingsProceedings of the 30th Annual ACM Symposium on Applied Computing10.1145/2695664.2695866(901-907)Online publication date: 13-Apr-2015
  • (2015)Privacy-Preserving Naïve Bayesian Classifier-Based Recommendations on Distributed DataComputational Intelligence10.1111/coin.1201231:1(47-68)Online publication date: 1-Feb-2015
  • (2015)On the Privacy of Horizontally Partitioned Binary Data-Based Privacy-Preserving Collaborative FilteringRevised Selected Papers of the 10th International Workshop on Data Privacy Management, and Security Assurance - Volume 948110.1007/978-3-319-29883-2_13(199-214)Online publication date: 21-Sep-2015
  • (2015)Privacy Aspects of Recommender SystemsRecommender Systems Handbook10.1007/978-1-4899-7637-6_19(649-688)Online publication date: 2015
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