Modern search engines extensively personalize results by building detailed user profiles based on query history and behaviour. While personalization can enhance relevance, it introduces privacy risks and can lead to filter bubbles. This paper proposes and evaluates a lightweight, client-side query obfuscation strategy using randomly generated multilingual search queries to disrupt user profiling. Through controlled experiments on the Seznam.cz search engine, we assess the impact of interleaving real queries with obfuscating noise in various language configurations and ratios. Our findings show that while displayed search results remain largely stable, the search engine's identified user interests shift significantly under obfuscation. We further demonstrate that such random queries can prevent accurate profiling and overwrite established user profiles. This study provides practical evidence for query obfuscation as a viable privacy-preserving mechanism and introduces a tool that enables users to autonomously protect their search behaviour without modifying existing infrastructure.

翻译：现代搜索引擎通过基于查询历史和行为构建详细的用户画像，广泛实现结果个性化。虽然个性化可以提升相关性，但也引入了隐私风险并可能导致信息茧房。本文提出并评估了一种轻量级、客户端的查询混淆策略，该策略利用随机生成的多语言搜索查询来干扰用户画像构建。通过在Seznam.cz搜索引擎上进行受控实验，我们评估了在不同语言配置和比例下，将真实查询与混淆噪声交错混合所产生的影响。我们的研究结果表明，尽管显示的搜索结果基本保持稳定，但搜索引擎识别的用户兴趣在混淆作用下会发生显著偏移。我们进一步证明，此类随机查询能够阻止准确的画像构建并覆盖已建立的用户画像。本研究为查询混淆作为一种可行的隐私保护机制提供了实证依据，并介绍了一种使用户能够自主保护搜索行为而无需修改现有基础设施的工具。