We present OasisDB, an oblivious and scalable RDBMS framework designed to securely manage relational data while protecting against access and volume pattern attacks. Inspired by plaintext RDBMSs, OasisDB leverages existing oblivious key value stores (KV-stores) as storage engines and securely scales them to enhance performance. Its novel multi-tier architecture allows for independent scaling of each tier while supporting multi-user environments without compromising privacy. We demonstrate OasisDB's flexibility by deploying it with two distinct oblivious KV-stores, PathORAM and Waffle, and show its capability to execute a variety of SQL queries, including point and range queries, joins, aggregations, and (limited) updates. Experimental evaluations on the Epinions dataset show that OasisDB scales linearly with the number of machines. When deployed with a plaintext KV-store, OasisDB introduces negligible overhead in its multi-tier architecture compared to a plaintext database, CockroachDB. We also compare OasisDB with ObliDB and Obliviator, two oblivious RDBMSs, highlighting its advantages with scalability and multi-user support.

Edge computing while bringing computation and data closer to users in order to improve response time, distributes edge servers in wide area networks resulting in increased communication latency between the servers. Synchronizing globally distributed edge servers, especially in the presence of Byzantine servers, becomes costly due to the high communication complexity of Byzantine fault-tolerant consensus protocols. In this paper, we present Ziziphus, a geo-distributed system that partitions edge servers into fault-tolerant zones where each zone processes transactions initiated by nearby clients locally. Global synchronization among zones is required only in special situations, e.g., migration of clients from one zone to another. On the one hand, the two-level architecture of Ziziphus confines the malicious behavior of nodes within zones requiring a much cheaper protocol at the top level for global synchronization. On the other hand, Ziziphus processes local transactions within zones by edge servers closer to clients resulting in enhanced performance. Ziziphus further introduces zone clusters to enhance scalability where instead of running global synchronization among all zones, only zones of a single cluster are synchronized.

We present Waffle, a datastore that protects an application's data access patterns from a passive persistent adversary. Waffle achieves this without prior knowledge of the input data access distribution, making it the first of its kind to adaptively handle input sequences under a passive persistent adversary. Waffle maintains a constant bandwidth and client-side storage overhead, which can be adjusted to suit the application owner's preferences. This flexibility allows the owner to fine-tune system parameters and strike a balance between security and performance. Our evaluation, utilizing the Yahoo! Cloud Serving Benchmark (YCSB) benchmark and Redis as the backend storage, demonstrates promising results. The insecure baseline outperforms Waffle by a mere 5-6x, whereas Waffle outperforms Pancake-a state-of-the-art oblivious datastore under passive persistent adversaries-by 45-57%, and a concurrent ORAM system, TaoStore, by 102x.

Geo-distributed databases are the state of the art tools for managing cloud-based data. But maintaining hot records in geo-distributed databases such as Google's Spanner can be expensive, as it synchronizes each update across a majority of replicas. Frequent synchronization poses an obstacle to achieve high throughput for contentious updateheavy workloads. While such synchronizations are inevitable for complex data types, simple data types such as aggregate data can benefit from reduced synchronizations. To this end, we propose an alternate data management system, Samya, to manage aggregate cloud resource usage data. Samya disaggregates available resources and stores fractions of these resources across geo-distributed sites. Dis-aggregation allows sites to serve client requests independently without synchronization for each update. Samya incorporates a learning mechanism to predict future resource demands. If the predicted demand is not satisfied locally, a synchronization protocol, Avantan, is executed to redistribute available resources in the system. Avantan is a novel fault-tolerant consensus protocol where sites agree on the global availability of resources prior to redistribution. Experiments conducted on Google Cloud Platform highlight that dis-aggregating data and reducing synchronizations allows Samya to commit 16x to 18x more transactions than state of the art cloud geo-distributed systems such as Spanner and CockroachDB.

This errata article discusses and corrects a minor error in our work published in VLDB 2019. The discrepancy specifically pertains to Algorithms 3 and 4. The algorithms presented in the paper are biased towards a commit decision in a specific failure scenario. We explain the error using an example before correcting the algorithm.

Recently the long standing problem of optimal construction of quantile sketches was resolved by K arnin, L ang, and L iberty using the KLL sketch (FOCS 2016). The algorithm for KLL is restricted to online insert operations and no delete operations. For many real-world applications, it is necessary to support delete operations. When the data set is updated dynamically, i.e., when data elements are inserted and deleted, the quantile sketch should reflect the changes. In this paper, we propose KLL ± , the first quantile approximation algorithm to operate in the bounded deletion model to account for both inserts and deletes in a given data stream. KLL ± extends the functionality of KLL sketches to support arbitrary updates with small space overhead. The space bound for KLL ± is [EQUATION], where ∈ and δ are constants that determine precision and failure probability, and α bounds the number of deletions with respect to insert operations. The experimental evaluation of KLL ± highlights that with minimal space overhead, KLL ± achieves comparable accuracy in quantile approximation to KLL.

Large scale data management systems utilize State Machine Replication to provide fault tolerance and to enhance performance. Fault-tolerant protocols are extensively used in the distributed database infrastructure of large enterprises such as Google, Amazon, and Facebook. However, and in spite of years of intensive research, existing fault-tolerant protocols do not adequately address hybrid cloud environments consisting of private and public clouds which are widely used by enterprises. In this paper, we consider a private cloud consisting of nonmalicious nodes (crash-only failures) and a public cloud with possible malicious failures. We introduce SeeMoRe, a hybrid State Machine Replication protocol that uses the knowledge of where crash and malicious failures may occur in a public/private cloud environment to improve overall performance. SeeMoRe has three different modes that can be used depending on the private cloud load and the communication latency between the public and private clouds. SeeMoRe can dynamically transition from one mode to another. Furthermore, an extensive evaluation reveals that SeeMoRe’s performance is close to the state of the art crash fault-tolerant protocols while tolerating malicious failures.

The uprise of Bitcoin and other peer-to-peer cryptocurrencies has opened many interesting and challenging problems in cryptography, distributed systems, and databases. The main underlying data structure is blockchain, a scalable fully replicated structure that is shared among all participants and guarantees a consistent view of all user transactions by all participants in the system. In this tutorial, we discuss the basic protocols used in blockchain, and elaborate on its main advantages and limitations. To overcome these limitations, we provide the necessary distributed systems background in managing large scale fully replicated ledgers, using Byzantine Agreement protocols to solve the consensus problem. Finally, we expound on some of the most recent proposals to design scalable and efficient blockchains in both permissionless and permissioned settings. The focus of the tutorial is on the distributed systems and database aspects of the recent innovations in blockchains.

Data storage in the Cloud needs to be scalable and fault-tolerant. Atomic commitment protocols such as Two Phase Commit (2PC) provide ACID guarantees for transactional access to sharded data and help in achieving scalability. Whereas consensus protocols such as Paxos consistently replicate data across different servers and provide fault tolerance. Cloud based datacenters today typically treat the problems of scalability and fault-tolerance disjointedly. In this work, we propose a unification of these two different paradigms into one framework called Consensus and Commitment (C&C) framework. The C&C framework can model existing and well known data management protocols as well as propose new ones. We demonstrate the advantages of the C&C framework by developing a new atomic commitment protocol, Paxos Atomic Commit (PAC), which integrates commitment with recovery in a Paxos-like manner. We also instantiate commit protocols from the C&C framework catered to different Cloud data management techniques. In particular, we propose a novel protocol, Generalized PAC (G-PAC) that integrates atomic commitment and fault tolerance in a cloud paradigm involving both sharding and replication of data. We compare the performance of G-PAC with a Spanner-like protocol, where 2PC is used at the logical data level and Paxos is used for consistent replication of logical data. The experimental results highlight the benefits of combining consensus along with commitment into a single integrated protocol.

Bitcoin is a successful and interesting example of a global scale peer-to-peer cryptocurrency that integrates many techniques and protocols from cryptography, distributed systems, and databases. The main underlying data structure is blockchain, a scalable fully replicated structure that is shared among all participants and guarantees a consistent view of all user transactions by all participants in the cryptocurrency system. In this tutorial, we discuss the basic protocols used in blockchain, and elaborate on its main advantages and limitations. To overcome these limitations, we provide the necessary distributed systems background in managing large scale fully replicated ledgers, using Byzantine Agreement protocols to solve the consensus problem. Finally, we expound on some of the most recent proposals to design scalable and efficient blockchains. The focus of the tutorial is on the distributed systems and database technical aspects of the recent innovations in blockchains.