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This paper introduces Ibex, an advertising system that reduces the amount of data that is collected on users while still allowing advertisers to bid on real-time ad auctions and measure the effectiveness of their ad campaigns. Specifically, Ibex addresses an issue in recent proposals such as Google’s Privacy Sandbox Topics API in which browsers send information about topics that are of interest to a user to advertisers and demand-side platforms (DSPs). DSPs use this information to (1) determine how much to bid on the auction for a user who is interested in particular topics, and (2) measure how well their ad campaign does for a given audience (i.e., measure conversions). While Topics and related proposals reduce the amount of user information that is exposed, they still reveal user preferences. In Ibex, browsers send user information in an encrypted form that still allows DSPs and advertisers to measure conversions, compute aggregate statistics such as histograms about users and their interests, and obliviously bid on auctions without learning for whom they are bidding. Our implementation of Ibex shows that creating histograms is 1.7–2.5× more expensive for browsers than disclosing user information, and Ibex’s oblivious bidding protocol can finish auctions within 550 ms.more »We think this makes Ibex capable of preserving a good experience while improving user privacy.« less

Recent private information retrieval (PIR) schemes preprocess the database with a query-independent offline phase in order to achieve sublinear computation during a query-specific online phase. These offline/online protocols expand the set of applications that can profitably use PIR, but they make a critical assumption: that the database is immutable. In the presence of changes such as additions, deletions, or updates, existing schemes must preprocess the database from scratch, wasting prior effort. To address this, we introduce incremental preprocessing for offline/online PIR schemes, allowing the original preprocessing to continue to be used after database changes, while incurring an update cost proportional to the number of changes rather than the size of the database. We adapt two offline/online PIR schemes to use incremental preprocessing and show how it significantly improves the throughput and reduces the latency of applications where the database changes over time

Dopamine (DA) is an important neurotransmitter, which is essential for transmitting signals in neuronal communications. The deficiency of DA release from neurons is implicated in neurological disorders. There has been great interest in developing new optical probes for monitoring the release behavior of DA from neurons. H-aggregates of organic dyes represent an ordered supramolecular structure with delocalized excitons. In this paper, we use the self-assembly of 3,3′-diethylthiadicarbocyanine iodide (DiSC 2 (5)) in ammonia solution to develop crystalline H-aggregate nanoparticles, in which DiSC 2 (5) molecules show long-range π–π stacking. The crystalline H-aggregate nanoparticles are stable in cell culture medium and can serve as an efficient photo-induced electron transfer (PET) probe for the detection of DA with the concentration as low as 0.1 nM in cell culture medium. Furthermore, the crystalline H-aggregate nanoparticle-based PET probe is used to detect the release behavior of DA from the M17 human neuroblastoma cells. We find that the DA release from the cells is enhanced by nicotine stimulations. Our results highlight the potential of crystalline H-aggregate nanoparticle-based PET probes for diagnosing nervous system diseases and verifying therapies.

This paper introduces Mycelium, the first system to process differentially private queries over large graphs that are distributed across millions of user devices. Such graphs occur, for instance, when tracking the spread of diseases or malware. Today, the only practical way to query such graphs is to upload them to a central aggregator, which requires a great deal of trust from users and rules out certain types of studies entirely. With Mycelium, users' private data never leaves their personal devices unencrypted, and each user receives strong privacy guarantees. Mycelium does require the help of a central aggregator with access to a data center, but the aggregator merely facilitates the computation by providing bandwidth and computation power; it never learns the topology of the graph or the underlying data. Mycelium accomplishes this with a combination of homomorphic encryption, a verifiable secret redistribution scheme, and a mix network based on telescoping circuits. Our evaluation shows that Mycelium can answer a range of different questions from the medical literature with millions of devices.