Local cluster aggregation models of explosive percolation

We introduce perhaps the simplest models of graph evolution with choice that demonstrate discontinuous percolation transitions and can be analyzed via mathematical evolution equations. These models are local, in the sense that at each step of the process one edge is selected from a small set of potential edges sharing common vertices and added to the graph. We show that the evolution can be accurately described by a system of differential equations and that such models exhibit the discontinuous emergence of the giant component. Yet, they also obey scaling behaviors characteristic of continuous transitions, with scaling exponents that differ from the classic Erdos-Renyi model.Comment: Final version as appearing in PR

Weightless: Lossy Weight Encoding For Deep Neural Network Compression

The large memory requirements of deep neural networks limit their deployment and adoption on many devices. Model compression methods effectively reduce the memory requirements of these models, usually through applying transformations such as weight pruning or quantization. In this paper, we present a novel scheme for lossy weight encoding which complements conventional compression techniques. The encoding is based on the Bloomier filter, a probabilistic data structure that can save space at the cost of introducing random errors. Leveraging the ability of neural networks to tolerate these imperfections and by re-training around the errors, the proposed technique, Weightless, can compress DNN weights by up to 496x with the same model accuracy. This results in up to a 1.51x improvement over the state-of-the-art

More Practical and Secure History-Independent Hash Tables

Direct-recording electronic (DRE) voting systems have been used in several countries including United States, India, and the Netherlands to name a few. In the majority of those cases, researchers discovered several security flaws in the implementation and architecture of the voting system. A common property of the machines inspected was that the votes were stored sequentially according to the time they were cast, which allows an attacker to break the anonymity of the voters using some side-channel information. Subsequent research (Molnar et al. Oakland’06, Bethencourt et al. NDSS’07, Moran et al. ICALP’07) pointed out the connection between vote storage and history-independence, a privacy property that guarantees that the system does not reveal the sequence of operations that led to the current state. There are two flavors of history-independence. In a weakly history-independent data structure, every possible sequence of operations consistent with the current set of items is equally likely to have occurred. In a strongly history-independent data structure, items must be stored in a canonical way, i.e., for any set of items, there is only one possible memory representation. Strong history- independence implies weak history-independence but considerably constrains the design choices of the data structures. In this work, we present and analyze an efficient hash table data structure that simultaneously achieves the following properties: • It is based on the classic linear probing collision-handling scheme. • It is weakly history-independent. • It is secure against collision-timing attacks. That is, we consider adversaries that can measure the time for an update operation, but cannot observe data values, and we show that those adversaries cannot learn information about the items in the table. • All operations are significantly faster in practice (in particular, almost 2x faster for high load factors) than those of the commonly used strongly history-independent linear probing method proposed by Blelloch and Golovin (FOCS’07), which is not secure against collision-timing attacks. The first property is desirable for ease of implementation. The second property is desirable for the sake of maximizing privacy in scenarios where the memory of the hash table is exposed, such as post-election audit of DRE voting machines or direct memory access (DMA) attacks. The third property is desirable for maximizing privacy against adversaries who do not have access to memory but nevertheless are capable of accurately measuring the execution times of data structure operations. To our knowledge, our hash table construction is the first data structure that combines history-independence and protection against a form of timing attacks

Auditable Data Structures

The classic notion of history-independence guarantees that if a data structure is ever observed, only its current contents are revealed, not the history of operations that built it. This powerful concept has applications, for example, to e-voting and data retention compliance, where data structure histories should be private. The concept of weak history-independence (WHI) assumes only a single observation will ever occur, while strong history-independence (SHI) allows for multiple observations at arbitrary times. WHI constructions tend to be fast, but provide no repeatability, while SHI constructions provide unlimited repeatability, but tend to be slow. We introduce auditable data structures, where an auditor can observe data structures at arbitrary times (as in SHI), but we relax the unrealistic restriction that data structures cannot react to observations, since in most applications of history-independence, data owners know when observations have occurred. We consider two audit scenarios—secure topology, where an auditor can observe the contents and pointers of a data structure, and secure implementation, where an auditor can observe the memory layout of a data structure. We present a generic template for auditable data structures and, as a foundation for any auditable data structure, an Auditable Memory Manager (AMM), which is an efficient memory manager that translates any auditable data structure with a secure topology into one with a secure implementation. We give a prototype implementation that provides empirical evidence that the worst-case time running times of our AMM are 45× to 8,300× faster than those of a well-known SHI memory manager. Thus, auditable data structures provide a practical way of achieving time efficiency, as in WHI, while allowing for multiple audits, as in SHI

Using redundancy to cope with failures in a delay tolerant network

We consider the problem of routing in a delay tolerant net-work (DTN) in the presence of path failures. Previous work on DTN routing has focused on using precisely known network dy-namics, which does not account for message losses due to link failures, buffer overruns, path selection errors, unscheduled de-lays, or other problems. We show how to split, replicate, and erasure code message fragments over multiple delivery paths to optimize the probability of successful message delivery. We provide a formulation of this problem and solve it for two cases: a 0/1 (Bernoulli) path delivery model where messages are ei-ther fully lost or delivered, and a Gaussian path delivery model where only a fraction of a message may be delivered. Ideas from the modern portfolio theory literature are borrowed to solve the underlying optimization problem. Our approach is directly relevant to solving similar problems that arise in replica place-ment in distributed file systems and virtual node placement in DHTs. In three different simulated DTN scenarios covering a wide range of applications, we show the effectiveness of our ap-proach in handling failures