1. Side-Channel Attack on Encrypted Traffic
Network packets’ sizes and directions between user and a popular search engine
By acting as a normal user and eavesdropping traffic with sniffer pro
Collected in May 2012
Internet
Client
Encrypted Traffic
Server
Fixed pattern: identified input string
User Input
Observed Directional Packet Sizes a:
801→,
←54,
←509,
60→
00:
812→,
←505,
60→,
813→,
←507,
b-byte
s-byte
There are researches on how to locate the web applications, which is not our focus in our work.
The data are collected from popular search engine (Google).
NB.
These data were collected 2 years ago, currently, Google may already fix it. However, there may exist in other applications. Our goal is not to list the current existing side-channels, but to show the importance of such attacks.
The key is that, usually, user found the side-channels and then company fixed it. Our solution may assist the designer and programmer of an application in pre-considering such side-channels and solve it in the designing process.
The data were collected in one type of setting (browsers, platforms and where to collect (the victim machine, or LAN, or WLAN…)), however, it is general enough to show the problem.
Indicator of the input itself

2. Longer Inputs, More Unique the Patterns
S value for each character entered as:
Second keystroke:
First Keystroke
Second Keystroke a b c d
509
487
493
501
497
504
516
488
482
481
502
473
477
543
478
499
Left table: for the first keystroke
Right table:
second column: a-d as the first keystroke,
3-6 columns: a-d as second keystroke following a-d, correspondingly.
For right table:
If the adversary only observe the traffic for second keystroke, s/he can distinguish 12 out of 16.
For example, when s=493, the adversary knows it is : (a)b
By combining two observations (traffic for both first and second keystrokes), s/he can completely distinguish all the possiblilities.
Of course, in reality, it may take more than two keystrokes to uniquely identify an input string.
16 out of 16
12 out of 16
The unique patterns leak out users’ private information: the input string

3. Re-Identification Attack
In publishing sensitive data, deleting explicit identifiers is not sufficient
Due to re-identification (or linking) attack
Using quasi-identifiers, in this
Case DOB, Zip code, and Gender
Medical data
Voter list
Name
DOB
Zip code
Gender
Bob
22031 M
Alice
Eve
Malice
...
SSN
DOB
Zip code
Gender
Disease
deleted
22031 M
AIDS
Hypertension
68-1-1
Toothache F
Headache
...

4. Apply the Idea to PPTP
Ceiling padding: pad every packet to the maximum size in the group
PPTP:
Padding
group
PPTP goals:
Privacy
Cost
S Value
Padding
(Prefix) char
Option 1
Option 2
473
477
478
(c) c
(c) d
499
(d) b
516
(d) d
501
(a) c
(b) a
PPDP:
Anonymized
group
PPDP goals:
Privacy
Data utility
Ceiling padding
Quasi-ID
Function 1
Function 2
Sensitive Attribute
Generalization
So we can borrow the concept from PPDP and apply to PPTP: k-indistinguishability (k is an integer) is satisfied if the size of every padding group is no less than k
However, there are a few difference, and hence challenges...