The remote file synchronization problem is how to update an outdated version of a file located on one machine to the current version located on another machine with a minimal amount of network communication. It arises in many scenarios including web site mirroring, file system backup and replication, or web access over slow links. A widely used open-source tool called rsync uses a single round of messages to solve this problem (plus an initial round for exchanging meta information). While research has shown that significant additional savings in bandwidth are possible by using multiple rounds, such approaches are often not desirable due to network latencies, increased protocol complexity, and higher I/O and CPU overheads at the endpoints. In this paper, we study single-round synchronization techniques that achieve savings in bandwidth consumption while preserving many of the advantages of the rsync approach. In particular, we propose a new and simple algorithm for file synchronization based on set reconciliation techniques. We then show how to integrate sampling techniques into our approach in order to adaptively select the most suitable algorithm and parameter setting for a given data set. Experimental results on several data sets show that the resulting protocol gives significant benefits over rsync, particularly on data sets with high degrees of redundancy between the versions. 1

Remote file synchronization has been studied extensively over the last decade, and the existing approaches can be divided into single-round and multi-round protocols. Single-round protocols are preferable in scenarios involving small files and large network latencies (e.g., web access over slow links) due protocol complexity and computing and I/O overheads. The best-known algorithms which are used for synchronization of file systems across machines are rsync, set reconciliation, Remote Differential Compression & RSYNC based on erasure codes. In this paper we will discuss the remote file synchronization protocols and compare the performance of all these protocols on different data sets. Index Terms — Remote files synchronization (RSYNC),

This survey presents a general approach for handling secret biometric data in cryptographic applications. The generality manifests itself in two ways: we attempt to minimize the assumptions we make about the data, and to present techniques that are broadly applicable wherever biometric inputs are used. Because biometric data comes from a variety of sources that are mostly outside of anyone’s control, it is prudent to assume as little as possible about how they are distributed; in particular, an adversary may know more about a distribution than a system’s designers and users. Of course, one may attempt to measure some properties of a biometric distribution, but relying on such measurements in the security analysis is dangerous, because the adversary may have even more accurate measurements available to it. For instance, even assuming that some property of a biometric behaves according to a binomial distribution (or some similar discretization of the normal distribution), one could determine the mean of this distribution only to within ≈ 1 √ after taking n n samples; a well-motivated adversary can take more measurements, and thus determine the mean more accurately. Rather than assuming that some statistical information about the biometric input is available, we assume only that the input is unpredictable: i.e., that if an adversary is allowed a single guess at the value of the input, the likelihood that it is correct is 2−m for some m. This is a minimal assumption in the applications we consider: indeed, if the input is easily guessed, then one cannot use it to derive, say, a secret key for encryption or remote authentication. Of course, determining the exact value of m may in itself present a challenge; however, some lower bound on m is necessary for any sort of security claims. Similarly, while some understanding of errors in biometric measurements is possible, we prefer to minimize the assumptions we make about such errors. We assume only that a subsequent measurement is within a given, allowed distance of the measurement taken at enrollment. The broad applicability of the approaches presented here stems from the initial observation that many prior solutions for specific security problems based on noisy data (including biometrics)