Traditional approaches to privacy are usually based on top of anonymizing or pseudonymizing systems. For example, users who would like to protect their identity and/or hide their activities while browsing the web frequently use anonymizing systems (e.g., Tor) or services (e.g., VPNs and proxies). Although anonymizing systems are usually effective, recent revelations suggest that anonymization can be compromised and can be used to provide a false sense of security. In this paper we assume a world where anonymization is (practically) not possible. Imagine, for example, a community where the use of anonymizing systems is frowned upon or even forbidden. Is it possible for users to protect their privacy when they can not hide their identity? In this paper, we focus our question on users interested in follow- ing information channels in microblogging services and we show that it is possible for users to protect their privacy even if they can not hide their identity. To do so, we propose two obfuscation-based algorithms and quantify their effectiveness. We show that obfusca- tion can be used in such a way so that attackers can not use this service to increase their a priori knowledge on whether a user is interested in a channel or not.

1. Is Privacy possible without
Anonymity?
The case for microblogging services
Panagiotis (Panos) Papadopoulos
Brave Software Inc.
Antonis Papadogiannakis , Michalis Polychronakis, Evangelos P. Markatos

2. The news delivery model changed
Shift to microblogging services and news
aggregators for news consumption
Panagiotis (Panos) Papadopoulos
Source: Statista
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3. Publish-Subscribe Model
• Information providers (or channels):
- Artists - Politicians
- News channels or Journalists - Religious channels
- Health institutes or doctors - Other communities
• Users subscribe to/follow channels
• Receive interesting information in a timely manner
Panagiotis (Panos) Papadopoulos 3

4. What about users’ privacy?
• Based on the user’s channel subscriptions, the service provider learns
the user’s interests:
• Political preferences (e.g., by following Donald Trump)
• Health issues (e.g., by following Alcoholic Anonymous)
• Detailed user profiling
• Can be used for many purposes
• Beyond the control of the users
• Privacy-sensitive channels
Panagiotis (Panos) Papadopoulos 4

5. Threat model
• a service that enables users to receive personalized news about their topics of interest
Ønews aggregators and RSS readers (e.g., Digg, Apple News, Google News, Reddit)
Ømicroblogging services (e.g., Twitter, Tout, Tumblr, Weibo)
• users subscribe themselves to individual channels of information that distribute content
about various topics
• honest-but-curious microblogging service that tracks users interests by monitoring the
channels they follow
• Collected user data is property of the service operator and
can be later passed/sold to third parties (advertisers, DMPs)
Panagiotis (Panos) Papadopoulos 5

6. How can user protect their privacy?
• Pseudonym or fake account
• IP address, third-party cookies, can reveal user’s identity
• Fake account +
• Device fingerprints can correlate anonymous and eponymous browsing
sessions
• Fake account + + VM per browsing session
• Too complex for ordinary users and mobile devices
Panagiotis (Panos) Papadopoulos 6

7. In a world where anonymity is
(practically) not possible:
Can we still protect the privacy of users of a microblogging service?
Panagiotis (Panos) Papadopoulos 7

8. Our approach
An obfuscation-based approach to preserve the privacy of the users’
interests:
1. user clicks to follow a channel S
2. browser extension follows on the background N other noise
channels (say S1, S2, ..., SN )
3. browser extension filters-out noise channels during rendering time
Panagiotis (Panos) Papadopoulos 8

9. Example of the tracker’s view
N=2
Real choicesNoise
Panagiotis (Panos) Papadopoulos 9

10. Tracker’s confidence
• A priori confidence (PU
pr(S)):
Øbefore user U follows channel S tracker has some confidence that U is interested in S
• general background information available (e.g. what percentage of people are alcoholics),
• background information available specifically tailored to the profile of U (e.g. what percentage of
white males between 40 and 50 are alcoholics)
• A posteriori confidence (PU
po(S)):
Øafter U follows channel S tracker has a new confidence level that U is interested in S.
ØFor example:
• if U follows only S and no noise channels (PU
po(S) = 1)
• if U follows S along with 1 noise channel (say S1), the tracker will know that U is interested either in
S or S1 (PU
po(S) = PU
po(S1) = 0.5)
Panagiotis (Panos) Papadopoulos 10

11. Our scope:
• Tracker would like to:
• increase as much as possible the a posteriori confidence that U is interested
in S
• i.e PU
po (S) ≫ PU
pr (S)
• We would like to:
• reduce a posteriori confidence to the level where it is no higher than the a
priori confidence
• i.e. PU
po (S) = PU
pr (S)
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12. How much noise should be added?
Example:
• tracker 10% confident that U is interested in channel S => PU
pr(S) = 0.1
• By adding 9 noise channels (+ 1 real = 10 channels) tracker will still be about 10%
confident (a posteriori confidence) that user S is the “real” channel
üAdd ⌈1/PU
pr (S)⌉ − 1 noise channels so PU
po (S) = 1/ ⌈1/PU
pr(S )⌉ ≈ PU
pr (S)
Panagiotis (Panos) Papadopoulos 12

13. What kind of noise should be added?
User U must be interested in following all noise channels with the same level
of interest she is interested in following channel S.
• Most likely a 80 years old metalhead uses channel of Justin Bieber as noise
(don’t his songs sound a bit like that anyway? !)
So:
• a priori probability that U is interested in S should be
• same as the a priori probability that U is interested in S1
• same as the a priori probability that U is interested in S2
• …
• PU
pr(S) = PU
pr(S1) = PU
pr(S2) = ... = PU
pr(SN)
Panagiotis (Panos) Papadopoulos 13

14. Challenge in noise selection
PU
pr(S) = PU
pr(S1) = PU
pr(S2) = ... = PU
pr(SN)
• Good luck finding such channels…
• Constant amount of noise… sounds like k-anonymity
• What is the proper k though?
qLow value of k will increase confidence of tracker
qHigh value of k will impose unnecessary load to the service
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15. Bucketing
When user U is interested in following 1 channel from the bucket, she
is instructed to follow all channels of this bucket
ØSize of each bucket is not be constant: depends on the lowest
channel probability contained in the bucket.
Example:
Øif user U interested in channel S with probability 10%, the bucket
should contain 10 channels.
• If it contains less , this will increase confidencePU
po(S) >> 10%
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16. Bounds in the error estimation (1/2)
So far we assumed that we always know the probability that U is
interested in S: PU
pr (S)
But:
• what if we do not?
• or we do not accurately estimate it?
• or tracker has some background knowledge
How is this expected to influence our methodology?
Panagiotis (Panos) Papadopoulos 16

17. Bounds in the error estimation (2/2)
Assume channel S of a political party (general feeling: PU
pr(S)=10% of voters)
1. Overestimation of a priori probability:
• Tracker knows: people like user U are 2x less likely to vote for this party (PAu
pr(S)=5%)
• PU
po(S) = 10% > PAu
pr(S) = 5% -> Our algorithm uses 9 noise channels instead of 19 (tracker’s confident increased)
2. Underestimation of a priori probability:
• Tracker knows: people like user U are 2x more likely to vote for this party (PAu
pr(S)=20%)
• PU
po(S) = 10% < PAu
pr(S) = 20% -> Our algorithm uses 9 noise channels instead of 5 (we are golden!)
So if:
• A priori is overestimated by a factor of f this will increase the tracker’s belief by no more than a factor of f
• A priori is underestimated by a factor of f, this won’t increase the tracker’s belief
Panagiotis (Panos) Papadopoulos 17

18. Conclusion
üwe explored whether it is possible for users to retrieve sensitive
information (about health issues, sexual preferences) without revealing
their interests
• While consuming information from microblogging services or news aggregators.
üwe proposed an obfuscation-based algorithm which aims to decrease the
confidence of the service provider when it infers the user’s interests and
we analyzed its effectiveness
ü we showed that it is possible for users to protect their
privacy even if they can not hide their identity.
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19. Background Slides
Panagiotis (Panos) Papadopoulos 19

20. Correlation Attack
The longer a user interacts with a system, the more
information she reveals
Example:
• user U is already following channel S (+noise channels).
• U is interested in following channel T with content semantically
related with the content of S.
ØTracker identifies correlation and also that U is interested in channel S
and not in any of the noise ones.
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21. Mitigation against Correlation Attack
What is the definition of semantically related channels?
• P(S) is the probability that the user U is interested in channel S
• P(S|T) is the probability that U is actually interested in an already followed
channel S given that she now follows channel T.
So two channels are semantically related if: P(S|T). P(S)
Our approach: P(S|T) = P(S1|T1) = P(S2|T2) = ... = P(SN-1|TN-1)
• where, S1, S2,..., SN-1 already followed noise channels for sensitive channel S.
• S1 and T1, S2 and T2,..., SN-1 and TN-1 are also correlated channels.
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22. Bucket creation algorithm
1. order all channels C according to their a priori probability PU
pr(C) in
decreasing order.
2. construct a set of channels with probabilities in the range [1/2, 1).
• If there are more than two such channels create buckets of size two.
• Each bucket contains at least 2 channels with probability at least 1/2.
3. construct a set of channels with probabilities in the range [1/3, 1/2).
• If there are more than three such channels in this range, create buckets of size 3.
• If, however, no three such channels are found, the algorithm will try to find four
channels in the range [1/4, 1/2).
• If unsuccessful, it will try to find five channels in the range [1/5, 1/2), etc.
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