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Compliant mechanisms with reconfigurable degrees of freedom are gaining attention in the development of kinesthetic haptic devices, robotic systems, and mechanical metamaterials. However, available devices exhibit limited programmability and form-customizability, restricting their versatility. To address this gap, we propose a metastructure concept featuring reconfigurable motional freedom and tunable stiffness, adaptable to various form factors and applications. These devices incorporate passive flexures and actively stiffness-changing rods to modify kinematic freedom. A rational design pipeline informs the flexures’ topological arrangements, geometric parameters, and control signals based on targeted mobilities, enabling the creation of unitary joints with up to six degrees of freedom. Our demonstrative application examples include a wrist device that has an effective stiffness of 0.370 Nm/deg (unlocked state, 5% displacement) to 2.278 Nm/deg (locked state, 1% displacement) to enable dynamic joint mobility control, a haptic thimble device (2.27-52.815 Nmm⁻¹at 1% displacement) that mimics the sensation of touching physical materials ranging from soft gel to metal surfaces, and a wearable device composed of multiple joints tailored for the arm and hand to augment haptic experiences or facilitate muscle training. We believe the presented method can help democratize compliant metastructures development and expand their versatility for broader contexts.

 

Morphing matter that change shapes and properties in response to external stimuli have gained significant interests in material science, robotics, biomedical engineering, wearables, architecture, and design. Along with functional advances, there is growing pressure and interest in considering the environmental impact of morphing matter during its life cycle. The unique manufacturing and usage of morphing matter means that existing sustainable design frameworks and principles for general physical products may not apply directly. For example, manufacturing morphing matter often requires designing and predicting materials' behaviors over time, and using devices fabricated with morphing matter often involves harnessing renewable energy and self‐reconfiguration, which pose unique sustainability opportunities and challenges. This study reflects and summarizes the field's practice in sustainable manufacturing, transport, use, and end‐of‐life handling of morphing matter. The term “sustainable morphing matter” (SMM) is coined, suggesting that sustainability‐conscious factors can become an integral component of morphing matter. In addition, ways to apply sustainability‐conscious factors to augment the existing design pipeline of morphing matter are presented, and more quantitative and algorithmic‐level developments are needed to apply these factors rigorously to the design process.

 

This paper proposes Addax, a fast, verifiable, and private online ad exchange. When a user visits an ad-supported site, Addax runs an auction similar to those of leading exchanges; Addax requests bids, selects the winner, collects payment, and displays the ad to the user. A key distinction is that bids in Addax’s auctions are kept private and the outcome of the auction is publicly verifiable. Addax achieves these properties by adding public verifiability to the affine aggregatable encodings in Prio (NSDI’17) and by building an auction protocol out of them. Our implementation of Addax over WAN with hundreds of bidders can run roughly half the auctions per second as a non-private and non-verifiable exchange, while delivering ads to users in under 600 ms with little additional bandwidth requirements. This efficiency makes Addax the first architecture capable of bringing transparency to this otherwise opaque ecosystem. 

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. We think this makes Ibex capable of preserving a good experience while improving user privacy. 

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 

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.