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斯坦福和普林斯頓的研究人員發(fā)現(xiàn)，今天的用戶更可能點擊朋友或朋友的朋友在社交網(wǎng)絡上分享的鏈接。

根據(jù)這一點，研究人員發(fā)現(xiàn)很容易將一個匿名的瀏覽歷史與社交網(wǎng)絡上的公開信息對接起來，識別用戶的真實身份。

在論文《De-anonymizing Web Browsing Data with Social Networks》中，研究人員報告他們的算法對374套匿名瀏覽歷史記錄的測試取得了70%左右的成功率。

論文地址：http://randomwalker.info/publications/browsing-history-deanonymization.pdf

Can online trackers and network adversaries de-anonymize web browsing data readily available to them? We show— theoretically, via simulation, and through experiments on real user data—that de-identified web browsing histories can be linked to social media profiles using only publicly available data. Our approach is based on a simple observation: each person has a distinctive social network, and thus the set of links appearing in one’s feed is unique. Assuming users visit links in their feed with higher probability than a random user, browsing histories contain tell-tale marks of identity. We formalize this intuition by specifying a model of web browsing behavior and then deriving the maximum likelihood estimate of a user’s social profile. We evaluate this strategy on simulated browsing histories, and show that given a history with 30 links originating from Twitter, we can deduce the corresponding Twitter profile more than 50% of the time. To gauge the real-world e?ectiveness of this approach, we recruited nearly 400 people to donate their web browsing histories, and we were able to correctly identify more than 70% of them. We further show that several online trackers are embedded on su”ciently many websites to carry out this attack with high accuracy. Our theoretical contribution applies to any type of transactional data and is robust to noisy observations, generalizing a wide range of previous de-anonymization attacks. Finally, since our attack attempts to find the correct Twitter profile out of over 300 million candidates, it is—to our knowledge—the largestscale demonstrated de-anonymization to date.

我們展示從理論上講，通過模擬，并通過實驗對真正的用戶數(shù)據(jù)–確定的網(wǎng)頁瀏覽歷史，可以鏈接到社會媒體的個人簡介中只使用公開可用的數(shù)據(jù)。我們的方法是基于一個簡單的觀察:每個人都有獨特的社會網(wǎng)絡，因此，一套在飼料內(nèi)出現(xiàn)的鏈接中是獨一無二的。假設用戶訪問比隨機概率較高的飼料環(huán)節(jié)的用戶，瀏覽歷史記錄，包含講故事的身份標志。我們正式通過指定這個直覺的網(wǎng)頁瀏覽行為模型，然后推導的極大似然估計一個用戶的社會形象。我們評估這個策略對模擬的瀏覽歷史，和證明，給出了一個30鏈接來自Twitter的歷史，我們可以推斷出相應的推特主頁上超過50%的時間。收集真實世界的電子?這種方法的有效性，我們招募了近400人捐贈他們的網(wǎng)頁瀏覽歷史，和我們能夠正確地識別其中的超過70%。我們還表明，在線跟蹤器是嵌入式蘇“（很多網(wǎng)站上進行這種攻擊的高精度。我們的理論貢獻，適用于任何類型的事務數(shù)據(jù)和魯棒性，嘈雜的意見，概括范圍廣泛的以前去匿名攻擊。終于，因為我們的進攻試圖找到正確的Twitter配置文件從三億候選人，它是-就我們所知–largestscale證明德-匿名約會。