Personalization and privacy: a survey of privacy risks and remedies in personalization-based systems

Toch, Eran; Wang, Yan; Cranor, Lorrie Faith

doi:10.1007/s11257-011-9110-z

Cited by 164 publications

(139 citation statements)

References 54 publications

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“…However, there are also social risks to the wide deployment of recommenders, from the obvious risks to individual privacy, to more subtle risks to the structure of social relationships (Toch et al 2012). Recommender technology can help ameliorate these risks-but only if deployed thoughtfully.…”

Section: Hidden Dangersmentioning

confidence: 99%

Recommender systems: from algorithms to user experience

Konstan

Riedl

2012

User Model User-Adap Inter

592

285

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Since their introduction in the early 1990's, automated recommender systems have revolutionized the marketing and delivery of commerce and content by providing personalized recommendations and predictions over a variety of large and complex product offerings. In this article, we review the key advances in collaborative filtering recommender systems, focusing on the evolution from research concentrated purely on algorithms to research concentrated on the rich set of questions around the user experience with the recommender. We show through examples that the embedding of the algorithm in the user experience dramatically affects the value to the user of the recommender. We argue that evaluating the user experience of a recommender requires a broader set of measures than have been commonly used, and suggest additional measures that have proven effective. Based on our analysis of the state of the field, we identify the most important open research problems, and outline key challenges slowing the advance of the state of the art, and in some cases limiting the relevance of research to real-world applications.

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Section: Hidden Dangersmentioning

confidence: 99%

Recommender systems: from algorithms to user experience

Konstan

Riedl

2012

User Model User-Adap Inter

592

285

View full text Add to dashboard Cite

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“…or it may ask "Do you support government's decision to increase tax?". Survey participants answer the questions with their own ideas independently, and the responses are often private [44]. Source dependency is not the main issue in these studies [2].…”

Section: Related Workmentioning

confidence: 99%

Using humans as sensors: An estimation-theoretic perspective

Wang

Amin

Shen

et al. 2014

IPSN-14 Proceedings of the 13th International Symposium on Information Processing in Sensor Networks

167

108

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Abstract-The explosive growth in social network content suggests that the largest "sensor network" yet might be human.Extending the participatory sensing model, this paper explores the prospect of utilizing social networks as sensor networks, which gives rise to an interesting reliable sensing problem. In this problem, individuals are represented by sensors (data sources) who occasionally make observations about the physical world. These observations may be true or false, and hence are viewed as binary claims. The reliable sensing problem is to determine the correctness of reported observations. From a networked sensing standpoint, what makes this sensing problem formulation different is that, in the case of human participants, not only is the reliability of sources usually unknown but also the original data provenance may be uncertain. Individuals may report observations made by others as their own. The contribution of this paper lies in developing a model that considers the impact of such information sharing on the analytical foundations of reliable sensing, and embed it into a tool called Apollo that uses Twitter as a "sensor network" for observing events in the physical world. Evaluation, using Twitter-based case-studies, shows good correspondence between observations deemed correct by Apollo and ground truth.

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“…Another challenge is that due to the enormous computation and storage power of search engines, query disambiguation keeps improving, making it problematic for users to reuse the same obfuscation techniques over time. Although research in web search query obfuscation has gained the attention of researchers [1] [2] [3] [4] [5], studies have noted that web search query confidentiality continues to be a difficult problem, mainly due to the monetization of search results by search engines [6] [7].…”

Section: Introductionmentioning

confidence: 99%

Web Search Query Privacy, an End-User Perspective

Mivule¹

2017

JIS

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While search engines have become vital tools for searching information on the Internet, privacy issues remain a growing concern due to the technological abilities of search engines to retain user search logs. Although such capabilities might provide enhanced personalized search results, the confidentiality of user intent remains uncertain. Even with web search query obfuscation techniques, another challenge remains, namely, reusing the same obfuscation methods is problematic, given that search engines have enormous computation and storage resources for query disambiguation. A number of web search query privacy procedures involve the cooperation of the search engine, a nontrusted entity in such cases, making query obfuscation even more challenging. In this study, we provide a review on how search engines work in regards to web search queries and user intent. Secondly, this study reviews material in a manner accessible to those outside computer science with the intent to introduce knowledge of web search engines to enable non-computer scientists to approach web search query privacy innovatively. As a contribution, we identify and highlight areas open for further investigative and innovative research in regards to end-user personalized web search privacy-that is methods that can be executed on the user side without third party involvement such as, search engines. The goal is to motivate future web search obfuscation heuristics that give users control over their personal search privacy.

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Personalization and privacy: a survey of privacy risks and remedies in personalization-based systems

Cited by 164 publications

References 54 publications

Recommender systems: from algorithms to user experience

Recommender systems: from algorithms to user experience

Using humans as sensors: An estimation-theoretic perspective

Web Search Query Privacy, an End-User Perspective

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