We initiate the study of a new abstraction called incremental decentralized data archival (iDDA). Specifically, imagine that there is an ever-growing, massive database such as a blockchain, a comprehensive human knowledge base like Wikipedia, or the Internet archive. We want to build a decentralized archival system for such datasets to ensure long-term robustness and sustainability. We identify several important properties that an iDDA scheme should satisfy. First, to promote heterogeneity and decentralization, we want to encourage even weak nodes with limited space (e.g., users' home computers) to contribute. The minimum space requirement to contribute should be approximately independent of the data size. Second, if a collection of nodes together receive rewards commensurate with contributing a total of m blocks of space, then we want the following reassurances: 1) if m is at least the database size, we should be able to reconstruct the entire dataset; and 2) these nodes should actually be committing roughly m space in aggregate - specifically, when m is much larger than the data size, these nodes cannot store only one copy of the database, and be able to impersonate arbitrarily many pseudonyms and get unbounded rewards. We propose new definitions that mathematically formalize the aforementioned requirements of an iDDA scheme. We also devise an efficient construction in the random oracle model which satisfies the desired security requirements. Our scheme incurs only Õ(1) audit cost, as well as Õ(1) update cost for both the publisher and each node, where Õ(⋅) hides polylogarithmic factors. Further, the minimum space provisioning required to contribute is as small as polylogarithmic. Our construction exposes several interesting technical challenges. Specifically, we show that a straightforward application of the standard hierarchical data structure fails, since both our security definition and the underlying cryptographic primitives we employ lack the desired compositional guarantees. We devise novel techniques to overcome these compositional issues, resulting in a construction with provable security while still retaining efficiency. Finally, our new definitions also make a conceptual contribution, and lay the theoretical groundwork for the study of iDDA. We raise several interesting open problems along this direction.

We show that there exists a (strongly) history-independent two-choice balls-and-bins algorithm that supports both insertions and deletions on a set of up to \(m\) balls, while guaranteeing a maximum load of \(m/n + O(1)\) with high probability, and achieving an expected recourse of \(O(\log \log (m/n))\) per operation. To the best of our knowledge, this is the first history-independent solution to achieve nontrivial guarantees of any sort for \(m/n \ge \omega(1)\), and is the first fully dynamic solution (history independent or not) to achieve \(O(1)\) overload with \(o(m/n)\) expected recourse.

In recent decades, cloud computing as a service has emerged as a major computing paradigm. These services (e.g., Amazon EC2, Google Compute Engine, Azure Virtual Machines) all offer variations of the following basic model for how storage works: a compute instance can choose between placing data on something that resembles a local disk (e.g., Amazon EBS, Google Block Store, Azure Managed Disks) versus what we will refer to as a global disk (e.g., Amazon S3, Google GCS, Azure Blob Storage). The disks are distinguished by two features:

We study person-level differentially private (DP) mean estimation in the case where each person holds multiple samples. DP here requires the usual notion of distributional stability when all of a person’s datapoints can be modified. Informally, if n people each have m samples from an unknown d-dimensional distribution with bounded k-th moments, we show thatpeople are necessary and sufficient to estimate the mean up to distance α in ℓ2-norm under ε-differential privacy (and its common relaxations). In the multivariate setting, we give computationally efficient algorithms under approximate DP and computationally inefficient algorithms under pure DP, and our nearly matching lower bounds hold for the most permissive case of approximate DP. Our computationally efficient estimators are based on the standard clip-and-noise framework, but the analysis for our setting requires both new algorithmic techniques and new analyses. In particular, our new bounds on the tails of sums of independent, vector-valued, bounded-moments random variables may be of interest.

We construct differentially private estimators with low sample complexity that estimate the median of an arbitrary distribution over $\mathbb{R}$ satisfying very mild moment conditions. Our result stands in contrast to the surprising negative result of Bun et al. (FOCS 2015) that showed there is no differentially private estimator with any finite sample complexity that returns any non-trivial approximation to the median of an arbitrary distribution.