21 research outputs found
ReCon: Revealing and Controlling PII Leaks in Mobile Network Traffic
It is well known that apps running on mobile devices extensively track and
leak users' personally identifiable information (PII); however, these users
have little visibility into PII leaked through the network traffic generated by
their devices, and have poor control over how, when and where that traffic is
sent and handled by third parties. In this paper, we present the design,
implementation, and evaluation of ReCon: a cross-platform system that reveals
PII leaks and gives users control over them without requiring any special
privileges or custom OSes. ReCon leverages machine learning to reveal potential
PII leaks by inspecting network traffic, and provides a visualization tool to
empower users with the ability to control these leaks via blocking or
substitution of PII. We evaluate ReCon's effectiveness with measurements from
controlled experiments using leaks from the 100 most popular iOS, Android, and
Windows Phone apps, and via an IRB-approved user study with 92 participants. We
show that ReCon is accurate, efficient, and identifies a wider range of PII
than previous approaches.Comment: Please use MobiSys version when referencing this work:
http://dl.acm.org/citation.cfm?id=2906392. 18 pages, recon.meddle.mob
Assortment optimisation under a general discrete choice model: A tight analysis of revenue-ordered assortments
The assortment problem in revenue management is the problem of deciding which
subset of products to offer to consumers in order to maximise revenue. A simple
and natural strategy is to select the best assortment out of all those that are
constructed by fixing a threshold revenue and then choosing all products
with revenue at least . This is known as the revenue-ordered assortments
strategy. In this paper we study the approximation guarantees provided by
revenue-ordered assortments when customers are rational in the following sense:
the probability of selecting a specific product from the set being offered
cannot increase if the set is enlarged. This rationality assumption, known as
regularity, is satisfied by almost all discrete choice models considered in the
revenue management and choice theory literature, and in particular by random
utility models. The bounds we obtain are tight and improve on recent results in
that direction, such as for the Mixed Multinomial Logit model by
Rusmevichientong et al. (2014). An appealing feature of our analysis is its
simplicity, as it relies only on the regularity condition.
We also draw a connection between assortment optimisation and two pricing
problems called unit demand envy-free pricing and Stackelberg minimum spanning
tree: These problems can be restated as assortment problems under discrete
choice models satisfying the regularity condition, and moreover revenue-ordered
assortments correspond then to the well-studied uniform pricing heuristic. When
specialised to that setting, the general bounds we establish for
revenue-ordered assortments match and unify the best known results on uniform
pricing.Comment: Minor changes following referees' comment
The Convex Geometry of Linear Inverse Problems
In applications throughout science and engineering one is often faced with
the challenge of solving an ill-posed inverse problem, where the number of
available measurements is smaller than the dimension of the model to be
estimated. However in many practical situations of interest, models are
constrained structurally so that they only have a few degrees of freedom
relative to their ambient dimension. This paper provides a general framework to
convert notions of simplicity into convex penalty functions, resulting in
convex optimization solutions to linear, underdetermined inverse problems. The
class of simple models considered are those formed as the sum of a few atoms
from some (possibly infinite) elementary atomic set; examples include
well-studied cases such as sparse vectors and low-rank matrices, as well as
several others including sums of a few permutations matrices, low-rank tensors,
orthogonal matrices, and atomic measures. The convex programming formulation is
based on minimizing the norm induced by the convex hull of the atomic set; this
norm is referred to as the atomic norm. The facial structure of the atomic norm
ball carries a number of favorable properties that are useful for recovering
simple models, and an analysis of the underlying convex geometry provides sharp
estimates of the number of generic measurements required for exact and robust
recovery of models from partial information. These estimates are based on
computing the Gaussian widths of tangent cones to the atomic norm ball. When
the atomic set has algebraic structure the resulting optimization problems can
be solved or approximated via semidefinite programming. The quality of these
approximations affects the number of measurements required for recovery. Thus
this work extends the catalog of simple models that can be recovered from
limited linear information via tractable convex programming