Incubation of HIV with human whole, parotid, or submandibular saliva leads to a decrease in viral infectivity in Sup-T1 cells. The effect is most pronounced with submandibular saliva. Inhibition is seen within 2 min, and increases with time. There is little inhibition seen after incubation of saliva with HSV, and no effect with adenovirus, suggested that there is some viral specificity. Electron microscopic studies revealed that HIV-saliva aggregates are trapped in 0.45-microns pore size nitrocellulose filters. If these inhibitory effects are manifest in vivo, this could account for the low level of virus detected in oral secretions.

Curators of sensitive datasets sometimes need to know whether queries against the data are differentially private [Dwork et al. 2006]. Two sorts of logics have been proposed for checking this property: (1) type systems and other static analyses, which fully automate straightforward reasoning with concepts like "program sensitivity" and "privacy loss, " and (2) full-blown program logics such as apRHL (an approximate, probabilistic, relational Hoare logic) [Barthe et al. 2016], which support more flexible reasoning about subtle privacypreserving algorithmic techniques but offer only minimal automation.We propose a three-level logic for differential privacy in an imperative setting and present a prototype implementation called Fuzzi. Fuzzi's lowest level is a general-purpose logic; its middle level is apRHL; and its top level is a novel sensitivity logic adapted from the linear-logic-inspired type system of Fuzz, a differentially private functional language [Reed and Pierce 2010]. The key novelty is a high degree of integration between the sensitivity logic and the two lower-level logics: the judgments and proofs of the sensitivity logic can be easily translated into apRHL; conversely, privacy properties of key algorithmic building blocks can be proved manually in apRHL and the base logic, then packaged up as typing rules that can be applied by a checker for the sensitivity logic to automatically construct privacy proofs for composite programs of arbitrary size.We demonstrate Fuzzi's utility by implementing four different private machine-learning algorithms and showing that Fuzzi's checker is able to derive tight sensitivity bounds.

Applying differential privacy at scale requires convenient ways to check that programs computing with sensitive data appropriately preserve privacy. We propose here a fully automated framework for testing differential privacy, adapting a well-known łpointwisež technique from informal proofs of differential privacy. Our framework, called DPCheck, requires no programmer annotations, handles all previously verified or tested algorithms, and is the first fully automated framework to distinguish correct and buggy implementations of PrivTree, a probabilistically terminating algorithm that has not previously been mechanically checked.We analyze the probability of DPCheck mistakenly accepting a non-private program and prove that, theoretically, the probability of false acceptance can be made exponentially small by suitable choice of test size.We demonstrate DPCheck's utility empirically by implementing all benchmark algorithms from prior work on mechanical verification of differential privacy, plus several others and their incorrect variants, and show DPCheck accepts the correct implementations and rejects the incorrect variants.We also demonstrate how DPCheck can be deployed in a practical workflow to test differentially privacy for the 2020 US Census Disclosure Avoidance System (DAS).CCS Concepts: • Software and its engineering → Domain specific languages; Software testing and debugging; Dynamic analysis.

Recently, a number of systems have been deployed that gather sensitive statistics from user devices while giving differential privacy guarantees. One prominent example is the component in Apple's macOS and iOS devices that collects information about emoji usage and new words. However, these systems have been criticized for making unrealistic assumptions, e.g., by creating a very high "privacy budget" for answering queries, and by replenishing this budget every day, which results in a high worst-case privacy loss. However, it is not obvious whether such assumptions can be avoided if one requires a strong threat model and wishes to collect data periodically, instead of just once. In this paper, we show that, essentially, it is possible to have one's cake and eat it too. We describe a system called Honeycrisp whose privacy cost depends on how often the data changes, and not on how often a query is asked. Thus, if the data is relatively stable (as is likely the case, e.g., with emoji and word usage), Honeycrisp can answer periodic queries for many years, as long as the underlying data does not change too often. Honeycrisp accomplishes this by using a) the sparsevector technique, and b) a combination of cryptographic techniques to enable global differential privacy without a trusted party. Using a prototype implementation, we show that Honeycrisp is efficient and can scale to large deployments.

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