Privacy-focused browser Brave is working on its own search engine. It has bought Tailcat, an open-source engine created by a team who worked on the defunct anti-tracking browser and search engine Cliqz, to power Brave Search. The company will allow others to use Brave Search tech to build their own search engines. Brave says the search engine will provide an alternative to Google Search and Chrome. It's developing Brave Search using the same principles as its browser, which now has more than 25 million monthly active users.
So is DuckDuckGo no good? Surprised you did not mention it. Following last week's article about privacy and surveillance capitalism, several readers wrote in about the absence of DuckDuckGo, and it was mentioned a dozen times in the comments. I have suggested this privacy-oriented search engine a few times since 2012, and I think it's worth a go. However, I'm answering Murray's earlier query along the same lines because I can use his email verbatim rather than cobbling together a joint question from multiple sources.
This paper presents several arguments that the deletion or anonymization of search query data is problematic. A more balanced approach would lead to a more uniform solution to data protection in which maintaining search query privacy would not sacrifice the benefits of long-term, confidential storage of the data.
Google is an advertising company. And advertising revenue is always under siege -- from competitors, regulators and market forces. Today, around 70 percent of Google's revenue comes from advertising, according to a "featured snippet" on Google Search. Meanwhile, shareholders expect solid growth. But how do you achieve that with a fickle and fragile business like online advertising.
Collaborative query routing is a new paradigm for Web search that treats both established search engines and other publicly available indices as intelligent peer agents in a search network. The approach makes it transparent for anyone to build their own (micro) search engine, by integrating established Web search services, desktop search, and topical crawling techniques. The challenge in this model is that each of these agents must learn about its environment— the existence, knowledge, diversity, reliability, and trustworthiness of other agents — by analyzing the queries received from and results exchanged with these other agents. We present the 6S peer network, which uses machine learning techniques to learn about the changing query environment. We show that simple reinforcement learning algorithms are sufficient to detect and exploit semantic locality in the network, resulting in efficient routing and high-quality search results. A prototype of 6S is available for public use and is intended to assist in the evaluation of different AI techniques employed by the networked agents.