Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Analysis and Mitigations of Reverse Engineering Attacks on Local Feature
  Descriptors   As autonomous driving and augmented reality evolve, a practical concern is
data privacy. In particular, these applications rely on localization based on
user images. The widely adopted technology uses local feature descriptors,
which are derived from the images and it was long thought that they could not
be reverted back. However, recent work has demonstrated that under certain
conditions reverse engineering attacks are possible and allow an adversary to
reconstruct RGB images. This poses a potential risk to user privacy. We take
this a step further and model potential adversaries using a privacy threat
model. Subsequently, we show under controlled conditions a reverse engineering
attack on sparse feature maps and analyze the vulnerability of popular
descriptors including FREAK, SIFT and SOSNet. Finally, we evaluate potential
mitigation techniques that select a subset of descriptors to carefully balance
privacy reconstruction risk while preserving image matching accuracy; our
results show that similar accuracy can be obtained when revealing less
information.

24
The categories of this paper are: dg-ga
29
The categories of this paper are: funct-an
8
The categories of this paper are: hep-ex
34
The categories of this paper are: chem-ph
31
The categories of this paper are: ao-sci
17
The categories of this paper are: q-fin
25
The categories of this paper are: patt-sol
22
The categories of this paper are: chao-dyn
3
The categories of this paper are: physics
18
The categories of this paper are: econ
30
The categories of this paper are: q-alg
36
The categories of this paper are: cmp-lg
27
The categories of this paper are: comp-gas
4
The categories of this paper are: cond-mat
19
The categories of this paper are: eess
33
The categories of this paper are: plasm-ph