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  1. Social media platforms curate access to information and opportunities, and so play a critical role in shaping public discourse today. The opaque nature of the algorithms these platforms use to curate content raises societal questions. Prior studies have used black-box methods led by experts or collaborative audits driven by everyday users to show that these algorithms can lead to biased or discriminatory outcomes. However, existing auditing methods face fundamental limitations because they function independent of the platforms. Concerns of potential harmful outcomes have prompted proposal of legislation in both the U.S. and the E.U. to mandate a new form of auditing where vetted external researchers get privileged access to social media platforms. Unfortunately, to date there have been no concrete technical proposals to provide such auditing, because auditing at scale risks disclosure of users' private data and platforms' proprietary algorithms. We propose a new method for platform-supported auditing that can meet the goals of the proposed legislation. The first contribution of our work is to enumerate the challenges and the limitations of existing auditing methods to implement these policies at scale. Second, we suggest that limited, privileged access to relevance estimators is the key to enabling generalizable platform-supported auditing of social media platforms by external researchers. Third, we show platform-supported auditing need not risk user privacy nor disclosure of platforms' business interests by proposing an auditing framework that protects against these risks. For a particular fairness metric, we show that ensuring privacy imposes only a small constant factor increase (6.34x as an upper bound, and 4× for typical parameters) in the number of samples required for accurate auditing. Our technical contributions, combined with ongoing legal and policy efforts, can enable public oversight into how social media platforms affect individuals and society by moving past the privacy-vs-transparency hurdle. 
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  4. The Domain Name System (DNS) is used in every website visit and e-mail transmission, so privacy is an obvious concern. In DNS, users ask recursive resolvers (or ``recursives'') to make queries on their behalf. Prior analysis of DNS privacy focused on privacy risks to individual end-users, mainly in traffic between users and recursives. Recursives cache and aggregate traffic for many users, factors that are commonly assumed to protect end-user privacy above the recursive. We document \emph{institutional privacy} as a new risk posed by DNS data collected at authoritative servers, even after caching and aggregation by DNS recursives. We are the first to demonstrate this risk by looking at leaks of e-mail exchanges which show communications patterns, and leaks from accessing sensitive websites, both of which can harm an institution's public image. We define a methodology to identify queries from institutions and identify leaks. We show the current practices of prefix-preserving anonymization of IP addresses and aggregation above the recursive are not sufficient to protect institutional privacy, suggesting the need for novel approaches. We demonstrate this claim by applying our methodology to real-world traffic from DNS servers that use partial prefix-preserving anonymization. Our work prompts additional privacy considerations for institutions that run their own resolvers and authoritative server operators that log and share DNS data. 
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