Transmitting on SSB (Single Side Band) radio requires a license for an operator except of in an emergency.Designed for long range communications at sea and for offshore vessels. An insulated backstay of a mast canserve as SSB antenna. Propagation of SSB radio waves work on reflected orrefracted back toward Earth from the ionosphere. Performance ranges in excess of 4000 miles.Some parts of SSB frequency spectrum work better at night, others during daytime.
The name of the permission is a filter based. For more details about filter based permissions, see OSGi Core Specification, Filter Based Permissions. The filter provides an access to all device service properties. Filter attribute names are processed in a case sensitive manner. For example, the operator can give a bundle the permission to only manage devices of vendor "acme":
ZigBee Operator v1 0 0 137 CRACKED]x[
Our join protocols are built around an efficient method to compute structured aggregations over a secret shared input vector V in D^n. If the parties have another secret-shared vector of control bits B in 0, 1 ^n to partition V into sub-vectors (that semantically relates to the join operations). A structured aggregation computes a secret shared vector V' in D^n where every sub-vector (V_b, ..., V_e) (defined by the control bits) is aggregated as V_i'= V_b op ... op V_i for i in b, ..., e according to some user-defined operator op. Critically, the b, e indices that partition the vector are secret. It's trivial to compute aggregations by sequentially processing the input vector and control bits. This would require O(n) rounds and would be very slow due to network latency. We introduce Aggregation Trees as a general technique to compute aggregations in O(log n) rounds. For our purpose of computing joins, we instantiate op in copy previous value, add, but we believe that this technique is quite powerful and can find applications in other useful settings.
Owner-centric control is a widely adopted method for easing owners' concerns over data abuses and motivating them to share their data out to gain collective knowledge. However, while many control enforcement techniques have been proposed, privacy threats due to the metadata leakage therein are largely neglected in existing works. Unfortunately, a sophisticated attacker can infer very sensitive information based on either owners' data control policies or their analytic task participation histories (e.g., participating in a mental illness or cancer study can reveal their health conditions). To address this problem, we introduce Vizard, a metadata-hiding analytic system that enables privacy-hardened and enforceable control for owners. Vizard is built with a tailored suite of lightweight cryptographic tools and designs that help us efficiently handle analytic queries over encrypted data streams coming in real-time (like heart rates). We propose extension designs to further enable advanced owner-centric controls (with AND, OR, NOT operators) and provide owners with release control to additionally regulate how the result should be protected before deliveries. We develop a prototype of Vizard that is interfaced with Apache Kafka, and the evaluation results demonstrate the practicality of Vizard for large-scale and metadata-hiding analytics over data streams.
Although query-based systems (QBS) have become one of the main solutions to share data anonymously, building QBSes that robustly protect the privacy of individuals contributing to the dataset is a hard problem. Theoretical solutions relying on differential privacy guarantees are difficult to implement correctly with reasonable accuracy, while ad-hoc solutions might contain unknown vulnerabilities. Evaluating the privacy provided by QBSes must thus be done by evaluating the accuracy of a wide range of privacy attacks. However, existing attacks against QBSes require time and expertise to develop, need to be manually tailored to the specific systems attacked, and are limited in scope. In this paper, we develop QuerySnout, the first method to automatically discover vulnerabilities in query-based systems. QuerySnout takes as input a target record and the QBS as a black box, analyzes its behavior on one or more datasets, and outputs a multiset of queries together with a rule to combine answers to them in order to reveal the sensitive attribute of the target record. QuerySnout uses evolutionary search techniques based on a novel mutation operator to find a multiset of queries susceptible to lead to an attack, and a machine learning classifier to infer the sensitive attribute from answers to the queries selected. We showcase the versatility of QuerySnout by applying it to two attack scenarios (assuming access to either the private dataset or to a different dataset from the same distribution), three real-world datasets, and a variety of protection mechanisms. We show the attacks found by QuerySnout to consistently equate or outperform, sometimes by a large margin, the best attacks from the literature. We finally show how QuerySnout can be extended to QBSes that require a budget, and apply QuerySnout to a simple QBS based on the Laplace mechanism. Taken together, our results show how powerful and accurate attacks against QBSes can already be found by an automated system, allowing for highly complex QBSes to be automatically tested "at the pressing of a button". We believe this line of research to be crucial to improve the robustness of systems providing privacy-preserving access to personal data in theory and in practice.
We evaluate back-and-forth exploration on 30 malware families. We build oracles for 4 families using Bitcoin for C&C and use them to demonstrate that back-and-forth exploration identifies 13 C&C signaling addresses missed by prior work, 8 of which are fundamentally missed by forward-only explorations. Our approach uncovers a wealth of services used by the malware including 44 exchanges, 11 gambling sites, 5 payment service providers, 4 underground markets, 4 mining pools, and 2 mixers. In 4 families, the relations include new attribution points missed by forward-only explorations. It also identifies relationships between the malware families and other cybercrime campaigns, highlighting how some malware operators participate in a variety of cybercriminal activities.
Several recent research efforts have proposed Machine Learning (ML)-based solutions that can detect complex patterns in network traffic for a wide range of network security problems. However, without understanding how these black-box models are making their decisions, network operators are reluctant to trust and deploy them in their production settings. One key reason for this reluctance is that these models are prone to the problem of underspecification, defined here as the failure to specify a model in adequate detail. Not unique to the network security domain, this problem manifests itself in ML models that exhibit unexpectedly poor behavior when deployed in real-world settings and has prompted growing interest in developing interpretable ML solutions (e.g., decision trees) for "explaining'' to humans how a given black-box model makes its decisions. However, synthesizing such explainable models that capture a given black-box model's decisions with high fidelity while also being practical (i.e., small enough in size for humans to comprehend) is challenging.
In this paper, we focus on synthesizing high-fidelity and low-complexity decision trees to help network operators determine if their ML models suffer from the problem of underspecification. To this end, we present Trustee, a framework that takes an existing ML model and training dataset as input and generates a high-fidelity, easy-to-interpret decision tree and associated trust report as output. Using published ML models that are fully reproducible, we show how practitioners can use Trustee to identify three common instances of model underspecification; i.e., evidence of shortcut learning, presence of spurious correlations, and vulnerability to out-of-distribution samples.
Clouds and massive-scale computing infrastructures are starting to dominate computing and will likely continue to do so for the foreseeable future. Major cloud operators are now comprising millions of cores hosting substantial fractions of corporate and government IT infrastructure. CCSW is the world's premier forum bringing together researchers and practitioners in all security aspects of cloud-centric and outsourced computing, including: Side channel attacks Cryptographic protocols for cloud security Secure cloud resource virtualization mechanisms Secure data management outsourcing (e.g., database as a service) Privacy and integrity mechanisms for outsourcing Foundations of cloud-centric threat models Secure computation outsourcing Remote attestation mechanisms in clouds Sandboxing and VM-based enforcements Trust and policy management in clouds Secure identity management mechanisms Cloud-aware web service security paradigms and mechanisms Cloud-centric regulatory compliance issues and mechanisms Business and security risk models and clouds Cost and usability models and their interaction with security in clouds Scalability of security in global-size clouds Binary analysis of software for remote attestation and cloud protection Network security (DOS, IDS etc.) mechanisms for cloud contexts Security for emerging cloud programming models Energy/cost/efficiency of security in clouds mOpen hardware for cloud Machine learning for cloud protection CCSW especially encourages novel paradigms and controversial ideas that are not on the above list. The workshop has historically acted as a fertile ground for creative debate and interaction in security-sensitive areas of computing impacted by clouds. This year marked the 13th anniversary of CCSW. In the past decade, CCSW has had a significant impact in our research community.
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