Practical security and privacy attacks against biometric hashing using sparse recovery

Topcu, Berkay; Karabat, Cagatay; Azadmanesh, Matin; Erdogan, Hakan

doi:10.1186/s13634-016-0396-1

EURASIP Journal on Advances in Signal Processing

Table 1 Existing biohash inversion attacks

From: Practical security and privacy attacks against biometric hashing using sparse recovery

Method	Assumptions	Security	Privacy
Multiply with the	- Random projection	Attack with biohash
pseudo-inverse of	matrix is available	from estimated features:
the random projection	- Threshold is fixed	- existing key
matrix [17, 33]	and it is 0	- a new key is assigned
	- Wavelet FMT face	and stolen again
	features
Genetic algorithms	- Random projection	1) Attack with biohash
[18]	matrix is available	from estimated features:
	- Threshold is fixed	- existing key
	and it is 0	- a new key is assigned
	- Fingercode features	and stolen again
		2) Average distance
		between real and
		approximated features
Perceptron-learning	- Several biohashes	Identification scenario,	Adversary has
with hill climbing and	of various different	where biohash generated	access to output
MLP modeling with	subjects are available	from each synthetic face	of feature extractor
customized hill-	(other methods assume	is matched against the	given a face image
climbing [19]	availability of a single	stolen templates	and applies hill-
	stolen biohash)		climbing attack to
	- Attacker can access		generate synthetic
	the matching scores of		face images
	the system
	- Secret key of the
	user is available
Solve a constrained	- Random projection	Attack with biohash	Reconstructed
minimization of	matrix is available	from estimated features:	face images
distance between	- Threshold is available	- existing key	from estimated
estimated features	- A database of	- a new key is assigned	vector using
and unrelated	unrelated features	and stolen again	PCA inversion
feature vector [4]	- Eigenface features
Methods proposed	- Random projection	1) Attack with biohash	Orthogonal linear
and discussed	matrix is available	from estimated features:	face features
in this study:	- Threshold is available	- existing key	(i.e., PCA, LDA):
	- Eigenface features	- a new key is assigned	transformation
- Sparse recovery		and is unknown	matrix is known
- Min-norm solutions		- a new key is assigned	and its inverse
		and stolen again	is used to
		2) Verification accuracy	reconstruct
		using the real features	face images
		as gallery and
		approximated features
		as probe

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