No abstract

Machine learning systems offer unparalled flexibility in dealing with evolving input in a variety of applications, such as intrusion detection systems and spam e-mail filtering. However, machine learning algorithms themselves can be a target of attack by a malicious adversary. This paper provides a framework for answering the question, "Can machine learning be secure?" Novel contributions of this paper include a taxonomy of different types of attacks on machine learning techniques and systems, a variety of defenses against those attacks, a discussion of ideas that are important to security for machine learning, an analytical model giving a lower bound on attacker's work function, and a list of open problems.

No abstract

Recent research has explored using Datalog-based languages to express a distributed system as a set of logical invariants [2,19]. Two properties of distributed systems proved difficult to model in Datalog. First, the state of any such system evolves with its execution. Second, deductions in these systems may be arbitrarily delayed, dropped, or reordered by the unreliable network links they must traverse. Previous efforts addressed the former by extending Datalog to include updates, key constraints, persistence and events, and the latter by assuming ordered and reliable delivery while ignoring delay. These details have a semantics outside Datalog, which increases the complexity of the language or its interpretation, and forces programmers to think operationally. We argue that the missing component from these previous languages is a notion of time.In this paper we present Dedalus, a foundation language for programming and reasoning about distributed systems. Dedalus reduces to a subset of Datalog [30] with negation, aggregate functions, successor and choice, and admits an explicit representation of time into the logic language. We show that Dedalus provides a declarative foundation for the two signature features of distributed systems: mutable state, and asynchronous processing and communication. Given these two features, we address three important properties of programs in a domain-specific manner: a notion of safety appropriate to non-terminating computations, stratified monotonic reasoning with negation over time, and efficient evaluation over time via a simple execution strategy. We also provide conservative syntactic checks for our temporal notions of safety and stratification. Our experience implementing full-featured systems in variants of Datalog suggests that Dedalus is well-suited to the specification of rich distributed services and protocols, and provides both cleaner semantics and richer tests of correctness.

Although flash storage has largely replaced hard disks in consumer class devices, enterprise workloads pose unique challenges that have slowed adoption of flash in "performance tier" storage appliances. In this paper, we describe Purity, the foundation of Pure Storage's Flash Arrays, the first all-flash enterprise storage system to support compression, deduplication, and high-availability.Purity borrows techniques from modern database and keyvalue storage architectures, and introduces novel storage primitives that have wide applicability to data management systems. For instance, all writes in Purity are monotonic, and deletions are handled using an atomic predicate-based tuple elision primitive.Purity's redundancy mechanisms are optimized for SSD failure modes and performance characteristics, allowing for fast recovery from component failures and lower space overhead than the best hard disk systems. We built deduplication and data compression schemes atop these primitives.Flash changes storage capacity/performance tradeoffs: unlike disk-based systems, flash deployments are rarely performance bound. A single Purity appliance can provide over 7 GiB/s of throughput on 32 KiB random I/Os, even through multiple device failures, and while providing asynchronous off-site replication. Typical installations have 99.9% latencies under 1 ms, and production arrays average 5.4× data reduction and 99.999% availability.Purity takes advantage of storage performance increasing more rapidly than computational performance to build a simpler (with respect to engineering, installation, and management) scale-up storage appliance that supports hundreds of terabytes of highly-available, high-performance storage. The resulting performance and capacity supports many customer deployments of multiple applications, including scaleout and parallel systems, such as MongoDB and Oracle RAC, on a single Purity appliance.