Paper 2023/1346

Street Rep: A Privacy-Preserving Reputation Aggregation System

Christophe Hauser, Dartmouth College
Shirin Nilizadeh, University of Texas at Arlington
Yan Shoshitaishvili, Arizona State University
Ni Trieu, Arizona State University
Srivatsan Ravi, University of Southern California
Christopher Kruegel, University of California, Santa Barbara
Giovanni Vigna, University of California, Santa Barbara

Over the last decade, online reputation has become a central aspect of our digital lives. Most online services and communities assign a reputation score to users, based on feedback from other users about various criteria such as how reliable, helpful, or knowledgeable a person is. While many online services compute reputation based on the same set of such criteria, users currently do not have the ability to use their reputation scores across services. As a result, users face trouble establishing themselves on new services or trusting each other on services that do not support reputation tracking. Existing systems that aggregate reputation scores, unfortunately, provide no guarantee in terms of user privacy, and their use makes user accounts linkable. Such a lack of privacy may result in embarrassment, or worse, place users in danger. In this paper, we present StreetRep, a practical system for aggregating user reputation scores in a privacy-preserving manner. StreetRep makes it possible for users to provide their aggregated scores over multiple services without revealing their respective identities on each service. We discuss our novel approach for tamper-proof privacy preserving score aggregation from multiple sources by combining existing techniques such as blind signatures, homomorphic signatures and private information retrieval. We discuss its practicality and resiliency against different types of attacks. We also built a prototype implementation of StreetRep. Our evaluation demonstrates that StreetRep (a) performs efficiently and (b) practically scales to a large user base.

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Publication info
Privacy preserving aggregation
Contact author(s)
Chris Hauser-2 @ dartmouth edu
shirin nilizadeh @ uta edu
yans @ asu edu
nitrieu @ asu edu
srivatsr @ usc edu
chris @ cs ucsb edu
vigna @ ucsb edu
2023-09-11: approved
2023-09-09: received
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      author = {Christophe Hauser and Shirin Nilizadeh and Yan Shoshitaishvili and Ni Trieu and Srivatsan Ravi and Christopher Kruegel and Giovanni Vigna},
      title = {Street Rep: A Privacy-Preserving Reputation Aggregation System},
      howpublished = {Cryptology ePrint Archive, Paper 2023/1346},
      year = {2023},
      note = {\url{}},
      url = {}
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