Numerous tools have been developed to aggressively block the execution of popular JavaScript programs in Web browsers. Such blocking also affects functionality of webpages and impairs user experience. As a consequence, many privacy preserving tools that have been developed to limit online tracking, often executed via JavaScript programs, may suffer from poor performance and limited uptake. A mechanism that can isolate JavaScript programs necessary for proper functioning of the website from tracking JavaScript programs would thus be useful. Through the use of a manually labelled dataset composed of 2,612 JavaScript programs, we show how current privacy preserving tools are ineffective in finding the right balance between blocking tracking JavaScript programs and allowing functional JavaScript code. To the best of our knowledge, this is the first study to assess the performance of current web privacy preserving tools in determining tracking vs. functional JavaScript programs. To improve this balance, we examine the two classes of JavaScript programs and hypothesize that tracking JavaScript programs share structural similarities that can be used to differentiate them from functional JavaScript programs. The rationale of our approach is that web developers often "borrow" and customize existing pieces of code in order to embed tracking (resp. functional) JavaScript programs into their webpages. We then propose one-class machine learning classifiers using syntactic and semantic features extracted from JavaScript programs. When trained only on samples of tracking JavaScript programs, our classifiers achieve accuracy of 99%, where the best of the privacy preserving tools achieve accuracy of 78%. The performance of our classifiers is comparable to that of traditional two-class SVM. One-class classification, where a training set of only tracking JavaScript programs is used for learning, has the advantage that it requires fewer labelled examples that can be obtained via manual inspection of public lists of well-known trackers. We further test our classifiers and several popular privacy preserving tools on a larger corpus of 4,084 websites with 135,656 JavaScript programs. The output of our best classifier on this data is between 20 to 64% different from the tools under study. We manually analyse a sample of the JavaScript programs for which our classifier is in disagreement with all other privacy preserving tools, and show that our approach is not only able to enhance user web experience by correctly classifying more functional JavaScript programs, but also discovers previously unknown tracking services.
Abstract. We propose a new protocol providing cryptographically secure authentication to unaided humans against passive adversaries. We also propose a new generic passive attack on human identification protocols. The attack is an application of Coppersmith's baby-step giantstep algorithm on human identification protcols. Under this attack, the achievable security of some of the best candidates for human identification protocols in the literature is further reduced. We show that our protocol preserves similar usability while achieves better security than these protocols. A comprehensive security analysis is provided which suggests parameters guaranteeing desired levels of security.
Aiming to reduce the cost and complexity of maintaining networking infrastructures, organizations are increasingly outsourcing their network functions (e.g., firewalls, traffic shapers and intrusion detection systems) to the cloud, and a number of industrial players have started to offer network function virtualization (NFV)-based solutions. Alas, outsourcing network functions in its current setting implies that sensitive network policies, such as firewall rules, are revealed to the cloud provider. In this paper, we investigate the use of cryptographic primitives for processing outsourced network functions, so that the provider does not learn any sensitive information. More specifically, we present a cryptographic treatment of privacy-preserving outsourcing of network functions, introducing security definitions as well as an abstract model of generic network functions, and then propose a few instantiations using partial homomorphic encryption and public-key encryption with keyword search. We include a proof-of-concept implementation of our constructions and show that network functions can be privately processed by an untrusted cloud provider in a few milliseconds. arXiv:1601.06454v1 [cs.CR] 25 Jan 2016 filters [3] naturally introduce false positives. Thus, occasionally, packets that do not match any policy are (mistakenly) dropped by the firewall. Furthermore, security/privacy of their solution is argued against a black-box assumption of Bloom filters, which does not analyze the security properties of Bloom filters themselves (such as one-wayness).Shi, Zhang, and Zhong [26] use multilinear maps from Coron, Lepoint and Tibouchi (CLT), which are based on graded encoding systems [9], to encode each bit of a firewall rule as a pair of level-1 encodings and a level-(n + 1) encoding for the whole rule, where n is the length of a possible packet. Following the security properties of the multilinear map, it is not possible to obtain level-i or lower encodings given a level-(i+1) encoding for each i. Upon receiving a packet, the encodings corresponding to the bits of the packet are multiplied and the result is then matched with the level-(n + 1) encoding for the whole policy through a procedure called isZero. Unfortunately, the CLT construction has been recently shown to be insecure, due to an attack on the isZero routine [7]; a key ingredient to check if a packet matches a policy.Although both these constructions focus specifically on outsourcing firewalls, they exclude details of how state tables can be maintained in their framework by a stateful firewall. Furthermore, due to being specific to firewalls, their solutions are only relevant to policies that result in a binary decision (allow or deny), excluding network functions that modify packet contents or perform more complex actions. Compared to these two solutions, our solutions for private NFV cover a much broader range of network functions, including firewalls, and also consider state tables.Private NFV also resembles real-time processing over encrypted packets. Th...
We present a differentially private mechanism to display statistics (e.g., the moving average) of a stream of real valued observations where the bound on each observation is either too conservative or unknown in advance. This is particularly relevant to scenarios of real-time data monitoring and reporting, e.g., energy data through smart meters. Our focus is on real-world data streams whose distribution is light-tailed, meaning that the tail approaches zero at least as fast as the exponential distribution. For such data streams, individual observations are expected to be concentrated below an unknown threshold. Estimating this threshold from the data can potentially violate privacy as it would reveal particular events tied to individuals [1]. On the other hand an overly conservative threshold may impact accuracy by adding more noise than necessary. We construct a utility optimizing differentially private mechanism to release this threshold based on the input stream. Our main advantage over the state-of-the-art algorithms is that the resulting noise added to each observation of the stream is scaled to the threshold instead of a possibly much larger bound; resulting in considerable gain in utility when the difference is significant. Using two real-world datasets, we demonstrate that our mechanism, on average, improves the utility by a factor of 3.5 on the first dataset, and 9 on the other. While our main focus is on continual release of statistics, our mechanism for releasing the threshold can be used in various other applications where a (privacy-preserving) measure of the scale of the input distribution is required.
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