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Time-Conditioned Action Anticipation in One Shot
No full textThe goal of human action anticipation is to predict future actions. Ideally, in real-world applications such as
video surveillance and self-driving systems, future actions
should not only be predicted with high accuracy but also
at arbitrary and variable time-horizons ranging from shortto long-term predictions. Current work mostly focuses on
predicting the next action and thus long-term prediction is
achieved by recursive prediction of each next action, which
is both inefficient and accumulates errors. In this paper, we
propose a novel time-conditioned method for efficient and
effective long-term action anticipation. There are two key
ingredients to our approach. First, by explicitly conditioning our anticipation network on time allows to efficiently
anticipate also long-term actions. And second, we propose
an attended temporal feature and a time-conditioned skip
connection to extract relevant and useful information from
observations for effective anticipation. We conduct extensive experiments on the large-scale Epic-Kitchen and the
50Salads Datasets. Experimental results show that the proposed method is capable of anticipating future actions at
both short-term and long-term, and achieves state-of-theart performance
Stellungnahme gegenüber dem Landtag des Freistaats Sachen in der Anhörung zur Drucksache 6/16724 „Gesetz zur Neuordnung der Informationssicherheit im Freistaat Sachsen“
Get PDFQRAT Polynomially Simulates \forall-Exp+Res
No full textThe proof system \forall-Exp+Res formally captures expansion-based solving of quantified Boolean formulas (QBFs) whereas the QRAT proof system captures QBF preprocessing. From previous work it is known that certain families of formulas have short proofs in QRAT, but not in \forall-Exp+Res. However, it was not known if the two proof systems were incomparable (i.e., if there also existed formulas with short \forall-Exp+Res proofs but without short QRAT proofs), or if QRAT polynomially simulates \forall-Exp+Res. We close this gap of the QBF proof-complexity landscape by presenting a polynomial simulation of \forall-Exp+Res in QRAT
PAtt: Physics-based Attestation of Control Systems
No full textEnsuring the integrity of embedded programmable logic controllers (PLCs) is critical for safe operation of industrial con-trol systems. In particular, a cyber-attack could manipulatecontrol logic running on the PLCs to bring the process ofsafety-critical application into unsafe states. Unfortunately,PLCs are typically not equipped with hardware support thatallows the use of techniques such as remote attestation to ver-ify the integrity of the logic code. In addition, so far remoteattestation is not able to verify the integrity of the physicalprocess controlled by the PLC.In this work, we present PAtt, a system that combines re-mote software attestation with control process validation. PAttleverages operation permutations—subtle changes in the op-eration sequences based on integrity measurements—whichdo not affect the physical process but yield unique traces ofsensor readings during execution. By encoding integrity mea-surements of the PLC’s memory state (software and data) intoits control operation, our system allows to remotely verifythe integrity of the control logic based on the resulting sensortraces. We implement the proposed system on a real PLCcontrolling a robot arm, and demonstrate its feasibility. Ourimplementation enables the detection of attackers that ma-nipulate the PLC logic to change process state and/or reportspoofed sensor readings (with an accuracy of 97% againsttested attacks)
Trapdoor Hash Functions and Their Applications
No full textWe introduce a new primitive, called {\em trapdoor hash functions} (TDH),
which are hash functions \mathsf{H}: \{0,1\}^n \rightarrow \{0,1\}^\sec with
additional trapdoor function-like properties. Specifically, given an index
, TDHs allow for sampling an encoding key \ek (that hides )
along with a corresponding trapdoor. Furthermore, given , a
hint value \mathsf{E}(\ek,x), and the trapdoor corresponding to \ek, the
bit of can be efficiently recovered. In this setting, one of our
main questions is: How small can the hint value \mathsf{E}(\ek,x) be? We
obtain constructions where the hint is only \emph{one bit} long based on DDH,
QR, DCR, or LWE.
This primitive opens a floodgate of applications for low-communication secure
computation.
We mainly focus on two-message protocols between a receiver and a sender, with
private inputs and , resp., where the receiver should learn .
We wish to optimize the \emph{(download) rate} of such protocols, namely the
asymptotic ratio between the size of the output and the sender's message.
Using TDHs, we obtain:
\begin{enumerate}
\item The first protocols for (two-message) \emph{rate-1 string OT} based
on DDH, QR, or LWE. This has several useful consequences, such as:
\begin{enumerate} \item The first constructions of PIR with communication
cost poly-logarithmic in the database size based on DDH or QR. These protocols
are in fact rate-1 when considering block PIR.
\item The first constructions of a \emph{semi-compact} homomorphic
encryption scheme for branching programs, where the encrypted output grows
only with the program {\em length}, based on DDH or QR.
\item The first constructions of lossy trapdoor functions with input to
output ratio approaching 1 based on DDH, QR or LWE.
\item The first {\em constant-rate} LWE-based construction of a 2-message
``statistically sender-private'' OT protocol in the plain model.
\end{enumerate}
\item The first {\em rate-1} protocols (under any assumption) for
parallel OTs and matrix-vector products from DDH, QR or LWE.
\end{enumerate}
We further consider the setting where evaluates a RAM program with
running time on . We obtain the first protocols with
communication sublinear in the size of , namely or
, based on DDH or, resp., pairings (and correlated-input
secure hash functions)
Fairwalk: Towards Fair Graph Embedding
Get PDFGraph embeddings have gained huge popularity in the recent years as a powerful tool to analyze social networks. However, no prior works have studied potential bias issues inherent within graph embedding. In this paper, we make a first attempt in this direction. In particular, we concentrate on the fairness of node2vec, a popular graph embedding method. Our analyses on two real-world datasets demonstrate the existence of bias in node2vec when used for friendship recommendation. We therefore propose a fairness-aware embedding method, namely Fairwalk, which extends node2vec. Experimental results demonstrate that Fairwalk reduces bias under multiple fairness metrics while still preserving the utility
Discovering Robustly Connected Subgraphs with Simple Descriptions
Get PDFWe study the problem of discovering robustly connected subgraphs that have simple descriptions. Our aim is, hence, to discover vertex sets which not only a) induce a subgraph that is difficult to fragment into disconnected components, but also b) can be selected from the entire graph using just a simple conjunctive query on their vertex attributes. Since many subgraphs do not have such a simple logical description, first mining robust subgraphs and post-hoc discovering their description leads to sub-optimal results. Instead, we propose to optimise over describable subgraphs only. To do so efficiently we propose a non-redundant iterative deepening approach, which we equip with a linear-time tight optimistic estimator that allows pruning large parts of the search space. Extensive empirical evaluation shows that our method can handle large real-world graphs, and discovers easily interpretable and meaningful subgraphs
MemGuard: Defending against Black-Box Membership Inference Attacks via Adversarial Examples
Get PDFIn a membership inference attack, an attacker aims to infer whether a data sample is in a target classifier's training dataset or not. Specifically, given a black-box access to the target classifier, the attacker trains a binary classifier, which takes a data sample's confidence score vector predicted by the target classifier as an input and predicts the data sample to be a member or non-member of the target classifier's training dataset. Membership inference attacks pose severe privacy and security threats to the training dataset. Most existing defenses leverage differential privacy when training the target classifier or regularize the training process of the target classifier. These defenses suffer from two key limitations: 1) they do not have formal utility-loss guarantees of the confidence score vectors, and 2) they achieve suboptimal privacy-utility tradeoffs.
In this work, we propose MemGuard, the first defense with formal utility-loss guarantees against black-box membership inference attacks. Instead of tampering the training process of the target classifier, MemGuard adds noise to each confidence score vector predicted by the target classifier. Our key observation is that attacker uses a classifier to predict member or non-member and classifier is vulnerable to adversarial examples. Based on the observation, we propose to add a carefully crafted noise vector to a confidence score vector to turn it into an adversarial example that misleads the attacker's classifier. Our experimental results on three datasets show that MemGuard can effectively defend against membership inference attacks and achieve better privacy-utility tradeoffs than existing defenses. Our work is the first one to show that adversarial examples can be used as defensive mechanisms to defend against membership inference attacks
The Hierarchy of Hyperlogics
Get PDFHyperproperties, which generalize trace properties by relating multiple traces, are widely studied in information-flow security. Recently, a number of logics for hyperproperties have been proposed, and there is a need to understand their decidability and relative expressiveness. The new logics have been obtained from standard logics with two principal extensions: temporal logics, like LTL and CTL∗, have been generalized to hyperproperties by adding variables for traces or paths. First-order and second-order logics, like monadic first-order logic of order and MSO, have been extended with the equal-level predicate. We study the impact of the two extensions across the spectrum of linear-time and branching-time logics, in particular for logics with quantification over propositions. The resulting hierarchy of hyperlogics differs significantly from the classical hierarchy, suggesting that the equal-level predicate adds more expressiveness than trace and path variables. Within the hierarchy of hyperlogics, we identify new boundaries on the decidability of the satisfiability problem. Specifically, we show that while HyperQPTL and HyperCTL∗ are both undecidable in general, formulas within their ∃∗∀∗ fragments are decidable
Canonical Representations of k-Safety Hyperproperties
Get PDFHyperproperties elevate the traditional view of trace properties form sets of traces to sets of sets of traces and provide a formalism for expressing information-flow policies. For trace properties, algorithms for verification, monitoring, and synthesis are typically based on a representation of the properties as omega-automata. For hyperproperties, a similar, canonical automata-theoretic representation is, so far, missing. This is a serious obstacle for the development of algorithms, because basic constructions, such as learning algorithms, cannot be applied. In this paper, we present a canonical representation for the widely used class of regular k-safety hyperproperties, which includes important polices such as noninterference. We show that a regular k-safety hyperproperty S can be represented by a finite automaton, where each word accepted by the automaton represents a violation of S. The representation provides an automata-theoretic approach to regular k-safety hyperproperties and allows us to compare regular k-safety hyperproperties, simplify them, and learn such hyperproperties. We investigate the problem of constructing automata for regular k-safety hyperproperties in general and from formulas in HyperLTL, and provide complexity bounds for the different translations. We also present a learning algorithm for regular k-safety hyperproperties based on the L* learning algorithm for deterministic finite automata