Notes on lattice points of zonotopes and lattice-face polytopes

Minkowski's second theorem on successive minima gives an upper bound on the volume of a convex body in terms of its successive minima. We study the problem to generalize Minkowski's bound by replacing the volume by the lattice point enumerator of a convex body. In this context we are interested in bounds on the coefficients of Ehrhart polynomials of lattice polytopes via the successive minima. Our results for lattice zonotopes and lattice-face polytopes imply, in particular, that for 0-symmetric lattice-face polytopes and lattice parallelepipeds the volume can be replaced by the lattice point enumerator.Comment: 16 pages, incorporated referee remarks, corrected proof of Theorem 1.2, added new co-autho

POSTER: Privacy-preserving Indoor Localization

Upcoming WiFi-based localization systems for indoor environments face a conflict of privacy interests: Server-side localization violates location privacy of the users, while localization on the user's device forces the localization provider to disclose the details of the system, e.g., sophisticated classification models. We show how Secure Two-Party Computation can be used to reconcile privacy interests in a state-of-the-art localization system. Our approach provides strong privacy guarantees for all involved parties, while achieving room-level localization accuracy at reasonable overheads.Comment: Poster Session of the 7th ACM Conference on Security & Privacy in Wireless and Mobile Networks (WiSec'14

Описание динамики движения валков лабораторной модели валкового грохота с вибрационным приводом

Вивчена динаміка коливальної системи валків лабораторної моделі валкового грохоту з вібраційним приводом. Виведено рівняння руху валків даної установки.Изучена динамика колебательной системы валков лабораторной модели валкового грохота с вибрационным приводом. Выведено уравнение движения валков данной установки

SCSlib: Transparently Accessing Protected Sensor Data in the Cloud

As sensor networks get increasingly deployed in real-world scenarios such as home and industrial automation, there is a similarly growing demand in analyzing, consolidating, and storing the data collected by these networks. The dynamic, on-demand resources offered by today’s cloud computing environments promise to satisfy this demand. However, prevalent security concerns still hinder the integration of sensor networks and cloud computing. In this paper, we show how recent progress in standardization can provide the basis for protecting data from diverse sensor devices when outsourcing data processing and storage to the cloud. To this end, we present our Sensor Cloud Security Library (SCSlib) that enables cloud service developers to transparently access cryptographically protected sensor data in the cloud. SCSlib specifically allows domain specialists who are not security experts to build secure cloud services. Our evaluation proves the feasibility and applicability of SCSlib for commodity cloud computing environments

IPAL: Breaking up Silos of Protocol-dependent and Domain-specific Industrial Intrusion Detection Systems

The increasing interconnection of industrial networks exposes them to an ever-growing risk of cyber attacks. To reveal such attacks early and prevent any damage, industrial intrusion detection searches for anomalies in otherwise predictable communication or process behavior. However, current efforts mostly focus on specific domains and protocols, leading to a research landscape broken up into isolated silos. Thus, existing approaches cannot be applied to other industries that would equally benefit from powerful detection. To better understand this issue, we survey 53 detection systems and find no fundamental reason for their narrow focus. Although they are often coupled to specific industrial protocols in practice, many approaches could generalize to new industrial scenarios in theory. To unlock this potential, we propose IPAL, our industrial protocol abstraction layer, to decouple intrusion detection from domain-specific industrial protocols. After proving IPAL's correctness in a reproducibility study of related work, we showcase its unique benefits by studying the generalizability of existing approaches to new datasets and conclude that they are indeed not restricted to specific domains or protocols and can perform outside their restricted silos

IPAL: Breaking up Silos of Protocol-dependent and Domain-specific Industrial Intrusion Detection Systems

The increasing interconnection of industrial networks exposes them to an ever-growing risk of cyber attacks. To reveal such attacks early and prevent any damage, industrial intrusion detection searches for anomalies in otherwise predictable communication or process behavior. However, current efforts mostly focus on specific domains and protocols, leading to a research landscape broken up into isolated silos. Thus, existing approaches cannot be applied to other industries that would equally benefit from powerful detection. To better understand this issue, we survey 53 detection systems and find no fundamental reason for their narrow focus. Although they are often coupled to specific industrial protocols in practice, many approaches could generalize to new industrial scenarios in theory. To unlock this potential, we propose IPAL, our industrial protocol abstraction layer, to decouple intrusion detection from domain-specific industrial protocols. After proving IPAL's correctness in a reproducibility study of related work, we showcase its unique benefits by studying the generalizability of existing approaches to new datasets and conclude that they are indeed not restricted to specific domains or protocols and can perform outside their restricted silos

Hi Doppelgänger: Towards Detecting Manipulation in News Comments

Public opinion manipulation is a serious threat to society, potentially influencing elections and the political situation even in established democracies. The prevalence of online media and the opportunity for users to express opinions in comments magnifies the problem. Governments, organizations, and companies can exploit this situation for biasing opinions. Typically, they deploy a large number of pseudonyms to create an impression of a crowd that supports specific opinions. Side channel information (such as IP addresses or identities of browsers) often allows a reliable detection of pseudonyms managed by a single person. However, while spoofing and anonymizing data that links these accounts is simple, a linking without is very challenging. In this paper, we evaluate whether stylometric features allow a detection of such doppelgängers within comment sections on news articles. To this end, we adapt a state-of-the-art doppelgänger detector to work on small texts (such as comments) and apply it on three popular news sites in two languages. Our results reveal that detecting potential doppelgängers based on linguistics is a promising approach even when no reliable side channel information is available. Preliminary results following an application in the wild shows indications for doppelgängers in real world data sets