# Tagvisor: A Privacy Advisor for Sharing Hashtags

arXiv: https://arxiv.org/abs/1802.04122 · HTML: https://mlsanity.com/paper/1802.04122/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Feb 2018.

## Built on by (most cited first, 8 of 8)

- [Auditing Membership Leakages of Multi-Exit Networks](https://mlsanity.com/paper/2208.11180/) (2022)
- [RetaGNN: Relational Temporal Attentive Graph Neural Networks for Holistic Sequential Recommendation](https://mlsanity.com/paper/2101.12457/) (2021)
- [Get a Model! Model Hijacking Attack Against Machine Learning Models](https://mlsanity.com/paper/2111.04394/) (2021)
- [FairSR: Fairness-aware Sequential Recommendation through Multi-Task Learning with Preference Graph Embeddings](https://mlsanity.com/paper/2205.00313/) (2022)
- [Privacy in Open Search: A Review of Challenges and Solutions](https://mlsanity.com/paper/2110.10720/) (2021)
- [Adversary for Social Good: Leveraging Adversarial Attacks to Protect Personal Attribute Privacy](https://mlsanity.com/paper/2306.02488/) (2023)
- [Consumer-side Fairness in Recommender Systems: A Systematic Survey of Methods and Evaluation](https://mlsanity.com/paper/2305.09330/) (2023)
- [Multi-modal Representation Learning for Social Post Location Inference](https://mlsanity.com/paper/2306.07935/) (2023)

## Abstract (arXiv)

Hashtag has emerged as a widely used concept of popular culture and campaigns, but its implications on people's privacy have not been investigated so far. In this paper, we present the first systematic analysis of privacy issues induced by hashtags. We concentrate in particular on location, which is recognized as one of the key privacy concerns in the Internet era. By relying on a random forest model, we show that we can infer a user's precise location from hashtags with accuracy of 70% to 76%, depending on the city. To remedy this situation, we introduce a system called Tagvisor that systematically suggests alternative hashtags if the user-selected ones constitute a threat to location privacy. Tagvisor realizes this by means of three conceptually different obfuscation techniques and a semantics-based metric for measuring the consequent utility loss. Our findings show that obfuscating as little as two hashtags already provides a near-optimal trade-off between privacy and utility in our dataset. This in particular renders Tagvisor highly time-efficient, and thus, practical in real-world settings.