Don't forget private retrieval: distributed private similarity search for large language models

While the flexible capabilities of large language models (LLMs) allow them to answer a range of queries based on existing learned knowledge, information retrieval to augment generation is an important tool to allow LLMs to answer questions on information not included in pre-training data. Such private information is increasingly being generated in a wide array of distributed contexts by organizations and individuals. Performing such information retrieval using neural embeddings of queries and documents always leaked information about queries and database content unless both were stored locally. We present Private Retrieval Augmented Generation (PRAG), an approach that uses multi-party computation (MPC) to securely transmit queries to a distributed set of servers containing a privately constructed database to return top-k and approximate top-k documents. This is a first-of-its-kind approach to dense information retrieval that ensures no server observes a client's query or can see the database content. The approach introduces a novel MPC friendly protocol for inverted file approximate search (IVF) that allows for fast document search over distributed and private data in sublinear communication complexity. This work presents new avenues through which data for use in LLMs can be accessed and used without needing to centralize or forgo privacy

Unstoppable Wallets: Chain-assisted Threshold ECDSA and its Applications

The security and usability of cryptocurrencies and other blockchain-based applications depend on the secure management of cryptographic keys. However, current approaches for managing these keys often rely on third parties, trusted to be available at a minimum, and even serve as custodians in some solutions, creating single points of failure and limiting the ability of users to fully control their own assets. In this work, we introduce the concept of unstoppable wallets, which are programmable threshold ECDSA wallets that allow users to co-sign transactions with a confidential smart contract, rather than a singular third-party. We propose a new model that encapsulates the use of a confidential smart contract as both a party and the sole (broadcast) communication channel in secure Multi-Party Computation (MPC) protocols. We construct highly efficient threshold ECDSA protocols that form the basis of unstoppable wallets and prove their security under this model, achieving the standard notion of fairness and robustness even in case of a dishonest majority of signers. Our protocols minimize the write-complexity for threshold ECDSA key-generation and signing, while reducing communication and computation overhead. We implement these protocols as smart contracts, deploy them on Secret Network, and showcase their applicability for two interesting applications, policy checking and wallet exchange, as well as their efficiency by demonstrating low gas costs and fees

Efficient secure computation enabled by blockchain technology

Thesis: S.M., Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2016.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Cataloged from student-submitted PDF version of thesis.Includes bibliographical references (pages 121-128).For several decades, secure multiparty computation has been the topic of extensive research, as it enables computing any functionality in a privacy-preserving manner, while ensuring correctness of the outputs. In recent years, the field has seen tremendous progress in terms of efficiency, although most results remained impractical for real applications concerning complex functionalities or significant data. When privacy is not a concern and we are only interested in achieving consensus in a distributed computing environment, the rise of cryptocurrencies, specifically Bitcoin, has presented an efficient and robust solution that exceeds the limits imposed by prior theoretical results. Primarily, Bitcoin's relative efficiency and superiority in achieving consensus is due to its inclusion of incentives. By doing so, it extends the standard cryptographic model to one that reasons about security through rationality of the different players. Inspired by this idea, this thesis focuses on the development of an efficient, general-purpose secure computation platform that relies on blockchain and cryptocurrencies (e.g., Bitcoin) for efficiency and scalability. Similar to how Bitcoin transformed the idea of distributed consensus, the goal in this work is to take secure multi-party computation from the realm of theory to practice. To that end, a formal model of secure computation in an environment of rational players is developed and is used to show how in this framework, efficiency is improved compared to the standard cryptographic model. The second part of this thesis deals with improving secure computation protocols over the integers and fixed-point numbers. The protocols and tools developed are a significant improvement over the current state-of-the-art, with an optimally efficient secure comparison protocol (for up to 64-bit integers) and better asymptotic bounds for fixed-point division.by Guy Zyskind.S.M

Campaign Optimization Through Behavioral Modeling and Mobile Network Analysis

© 2014 IEEE. Optimizing the use of available resources is one of the key challenges in activities that consist of interactions with a large number of "target individuals," with the ultimate goal of "winning" as many of them as possible, such as in marketing, service provision, political campaigns, or homeland security. Typically, the cost of interactions is monotonically increasing such that a method for maximizing the performance of these campaigns is required. In this paper, we propose a mathematical model to compute an optimized campaign by automatically determining the number of interacting units and their type, and how they should be allocated to different geographical regions in order to maximize the campaign's performance. We validate our proposed model using real world mobility data

Securing Physical Assets on the Blockchain : Linking a novel Object Identification Concept with Distributed Ledgers

The use of blockchain technology to track physical assets is not new. However, the state of the art concepts are not applicable due to several limitations. One limitation is the scalability of blockchains with regard to the number of transactions that can be processed by the network. The well-established technology in tracking products is based on RFID chips that can be cloned. This paper provides insights into how objects can be protected and monitored by a varnish with a unique crack pattern, as an example of a Physical Unclonable Function. The perceptual hash of the unique pattern is used to encrypt the associated data to ensure privacy. Instead of logging each event on the blockchain individually, which is not possible due to the limited transaction throughput, OriginStamp is used to preserve data integrity on the blockchain. OriginStamp aggregates events, combines them through hashing and embeds this hash into a Bitcoin transaction. Once the Bitcoin network mines the transaction into a block and confirms it, the timestamp is considered as immutable proof of existence. With this approach, the integrity of tracking data cannot be contested. In the future, the craquelure-based tracking approach could be extended to supply chain integration to secure the origin of products, including prevention of counterfeiting, securing the place of manufacture for trademark law or state surveillance of the agricultural economy.publishe