This paper presents Poplar, a new system for solving the private heavy-hitters problem. In this problem, there are many clients and a small set of data-collection servers. Each client holds a private bitstring. The servers want to recover the set of all popular strings, without learning anything else about any client's string. A web-browser vendor, for instance, can use Poplar to figure out which homepages are popular, without learning any user's homepage. We also consider the simpler private subset-histogram problem, in which the servers want to count how many clients hold strings in a particular set without revealing this set to the clients.
Poplar uses two data-collection servers and, in a protocol run, each client send sends only a single message to the servers. Poplar protects client privacy against arbitrary misbehavior by one of the servers and our approach requires no public-key cryptography (except for secure channels), nor general-purpose multiparty computation. Instead, we rely on incremental distributed point functions, a new cryptographic tool that allows a client to succinctly secret-share the labels on the nodes of an exponentially large binary tree, provided that the tree has a single non-zero path. Along the way, we develop new general tools for providing malicious security in applications of distributed point functions.
We present two new local differentially private algorithms for frequency estimation. One solves the fundamental frequency oracle problem; the other solves the well-known heavy hitters identification problem. Consistent with prior art, these are randomized algorithms. As a function of failure probability~$β$, the former achieves optimal worst-case estimation error for every~$β$, while the latter is optimal when~$β$ is at least inverse polynomial in~$n$, the number of users. In both algorithms, server running time is~$\tilde{O}(n)$ while user running time is~$\tilde{O}(1)$. Our frequency-oracle …
The discovery of heavy hitters (most frequent items) in user-generated data streams drives improvements in the app and web ecosystems, but can incur substantial privacy risks if not done with care. To address these risks, we propose a distributed and privacy-preserving algorithm for discovering the heavy hitters in a population of user-generated data streams. We leverage the sampling and thresholding properties of our distributed algorithm to prove that it is inherently differentially private, without requiring additional noise. We also examine the trade-off between privacy and utility, and sh…
Lightweight Bitcoin clients execute a Simple Payment Verification (SPV) protocol to verify the validity of transactions related to a particular user. Currently, lightweight clients use Bloom filters to significantly reduce the amount of bandwidth required to validate a particular transaction. This is despite the fact that research has shown that Bloom filters are insufficient at preserving the privacy of clients' queries. In this paper we describe our design of an SPV protocol that leverages Private Information Retrieval (PIR) to create fully private and performant queries. We show that our pr…
In several settings of practical interest, two parties seek to collaboratively perform inference on their private data using a public machine learning model. For instance, several hospitals might wish to share patient medical records for enhanced diagnostics and disease prediction, but may not be able to share data in the clear because of privacy concerns. In this work, we propose an actively secure protocol for outsourcing secure and private machine learning computations. Recent works on the problem have mainly focused on passively secure protocols, whose security holds against passive (`semi…
Content-Centric Networking (CCN) is an internetworking paradigm that offers an alternative to today's IP-based Internet Architecture. Instead of focusing on hosts and their locations, CCN emphasizes addressable named content. By decoupling content from its location, CCN allows opportunistic in-network content caching, thus enabling better network utilization, at least for scalable content distribution. However, in order to be considered seriously, CCN must support basic security services, including content authenticity, integrity, confidentiality, authorization and access control. Current appr…
In this work we describe a token-based solution to Contact Tracing via Distributed Point Functions (DPF) and, more generally, Function Secret Sharing (FSS). The key idea behind the solution is that FSS natively supports secure keyword search on raw sets of keywords without a need for processing the keyword sets via a data structure for set membership. Furthermore, the FSS functionality enables adding up numerical payloads associated with multiple matches without additional interaction. These features make FSS an attractive tool for lightweight privacy-preserving searching on a database of toke…
Private collection of statistics from a large distributed population is an important problem, and has led to large scale deployments from several leading technology companies. The dominant approach requires each user to randomly perturb their input, leading to guarantees in the local differential privacy model. In this paper, we place the various approaches that have been suggested into a common framework, and perform an extensive series of experiments to understand the tradeoffs between different implementation choices. Our conclusion is that for the core problems of frequency estimation and …
VisGuardian: A Lightweight Group-based Privacy Control Technique For Front Camera Data From AR Glasses in Home Environments
Tsinghua University（清华大学）；China Academy of Art（中国美术学院）；National University of Singapore（新加坡国立大学）；The Future Laboratory, Tsinghua University（清华大学未来实验室）；Beijing Academy of A（北京科学院）
