ACADEMIC ARTICLE SUMMARY
"You Might Also Like:" Privacy Risks of Collaborative Filtering
Article Source: 2011 IEEE Symposium on Security and Privacy, pp. 231-246, 2011
Publication Date:
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ARTICLE SUMMARY
Summary:
When one shops online, recommender systems often display related purchases by other users. Researchers designed a cyberattack using these systems to discover what users had bought. The success of the attack shows that these systems leak information.
POLICY RELEVANCE
Policy Relevance:
The use of large quantities of data drawn from private records can threaten privacy.
KEY TAKEAWAYS
Key Takeaways:
- Commercial websites such as Amazon.com use recommender systems to help consumers find related or recommended products.
- These systems are based on “collaborative filtering,” that is, the system makes recommendations based on patterns detected by observing other users’ behavior; for example, consumers that buy item X often buy item Y.
- Most systems use large quantities of private data aggregated from other users, but because the system displays only a list of items (but not information about users), most users do not think of these systems as a privacy risk.
- A cyberattack can use a little information about an individual consumer and the public output of a recommender system to infer the consumer’s purchases.
- The attacker notices the changes in the recommender’s output over time.
- Information about individual consumers can be collected from item reviews, or social networking sites like Facebook.
- Such an attack could be carried out by any Internet user.
- In one attack, the attacker targets a user, creates fake users with similar transaction history, and waits for “recommended items” to appear; it is likely that these are the target user’s purchases.
- Using similar attacks, researchers could infer private information from recommender displays. Attackers could:
- Guess user’s answers to secret questions on Hunch with 70% accuracy.
- Guess user’s music purchases from Last.fm with accuracy rates varying from 31% to 9%.
- Guess several users’ purchases on Amazon.com accurately.