Alambic: a privacy-preserving recommender system for electronic commerce
Alambic: a privacy-preserving recommender system for electronic commerce
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DOI：
10.1007/s10207-007-0049-3
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发表时间：
2008-09
影响因子：
3.2
通讯作者：
Esma Aïmeur;G. Brassard;José M. Fernandez;F. Onana
中科院分区：
文献类型：
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作者：
Esma Aïmeur;G. Brassard;José M. Fernandez;F. Onana
推荐系统使商家能够帮助顾客找到最能满足他们需求的产品。不幸的是，当前的推荐系统存在各种隐私保护漏洞。消费者应该能够保持他们的个人信息的私密性，包括他们的购买偏好，他们不应该被跟踪违背他们的意愿。商家的商业利益也应该得到保护，允许他们在不向第三方泄露合法收集的有价值信息的情况下做出准确的推荐。我们为一个名为Alambic的系统引入了一种理论方法，它在一个结合了基于内容、人口统计和协同过滤技术的混合推荐系统中实现了上述隐私保护目标。我们的系统在商家和半信任的第三方之间分割客户数据，因此任何一方都不能单独从他们的共享中获取敏感信息。因此，这一制度只能由这两个政党的联合来颠覆。
Recommender systems enable merchants to assist customers in finding products that best satisfy their needs. Unfortunately, current recommender systems suffer from various privacy-protection vulnerabilities. Customers should be able to keep private their personal information, including their buying preferences, and they should not be tracked against their will. The commercial interests of merchants should also be protected by allowing them to make accurate recommendations without revealing legitimately compiled valuable information to third parties. We introduce a theoretical approach for a system called Alambic, which achieves the above privacy-protection objectives in a hybrid recommender system that combines content-based, demographic and collaborative filtering techniques. Our system splits customer data between the merchant and a semi-trusted third party, so that neither can derive sensitive information from their share alone. Therefore, the system could only be subverted by a coalition between these two parties.