Alastair R. Beresford Frank Stajano University of Cambridge Presented by Arcadiy Kantor — CS4440 September 13, 2007

 Fifth-year CS major  Originally from Moscow, Russia, more recently from Alpharetta, GA  CS2200 Teaching Assistant  Opinions Editor, Technique  Highly involved in AIESECAIESEC

 Fourth Amendment to U.S. Constitution proclaims a right to privacy.  1948-Universal Declaration of Human Rights ◦ “Everyone has a right to privacy at home, with family, and in correspondence.”  Privacy on the internet and based upon new technologies is an ongoing issue.  One of the issues created by new technology is location privacy.

 The ability to prevent other parties from learning one’s current or past location.  The need is a recent development.  Pervasive computing applications may require certain location information.

 To protect the privacy of our location information while taking advantage of location-aware services.

 Location-based applications fall into three categories: 1. Applications that cannot work without the user’s identity. 2. Applications that can function completely anonymously. 3. Applications that cannot be accessed anonymously, but do not require the user’s true identity to function.

 While you trust the service provider and middleware, you do not trust any of the applications.  Therefore, you use the middleware to provide frequently-changing pseudonyms to the applications. ◦ Purpose: Not to establish reputation, but to provide a “return address.”

 Systems with high resolution ◦ Spatial ◦ Temporal  Can link old and new pseudonyms to one another.

 Mix network ◦ Store-and forward network used to anonymize communication. ◦ Hostile observers who can monitor all the links in the network cannot match up the sender and the receiver of a message.  Mix zones apply this concept to locations.

As you enter a mix zone, you are assigned a new pseudonym. The application no longer knows which user is which until you leave the mix zone with a new pseudonym.

 A mix zone’s security strongly depends on the number of users in it. ◦ If you are the only person in the mix zone, it provides zero anonymity.  Users moving in a direction are much more likely to continue moving the same way.  If two application zones are closer to one another than a third, the time of travel through the mix zone can reveal a user’s identity.

 Two measures ◦ Anonymity set (instant and average values) ◦ Entropy

 The group of people visiting a given mix zone at the same time as the user.  A rough determination of the level of privacy. ◦ i.e. a user may not wish to provide location updates to an application unless the anonymity set size is >= 20 people.  Average anonymity set size for current and neighboring mix zones can be used to estimate overall level of location privacy.

 Used installation of Active Bat system at AT&T Labs Cambridge. ◦ Each user carries a small “bat” device that provides location updates. ◦ System can locate bats with less than 3cm error up to 95 percent of the time. ◦ Typical update rate: 1-10 times per second.  Approximately 3.4 million samples taken over two weeks used for data.

Z1: first-floor hallway Z2: first-floor hallway and main corridor Z3: hallway, main corridor, stairwell on all floors.

Needed an 8-minute update period to provide anonymity set size of 2.

Needed only a 15-second update period to reach anonymity set size of 2. Much better, but still has issues.

 Level of privacy provided in experiment is rather low. ◦ High resolution of tracking system ◦ Low user population  May be significantly more effective for tracking systems based on locating cell phones via towers they use.

 The anonymity set’s size is only a good measure of anonymity when all the members of the set are equally likely to be the one of interest to an observer. ◦ i.e., an observer cannot narrow down the set of users by identifying patterns and trends. ◦ Maximum entropy.

 A user moving in a given direction is likely to keep moving in the same direction.  Suppose you define p as the user’s preceding location (location at time t-1) and s as the subsequent location (location at time t+1).  Can create a movement matrix to calculate the probabilities of movement from one zone to another.

Each element represents the frequency of movements from the preceding zone, p, at time t-1, to the subsequent zone, s, at time t+1.

 Conditional probability of coming out through zone s given that you have gone in through zone p:  Then the entropy can be calculated:

 Using the same set of results and the aforementioned formulas, one can calculate the probability of a person’s actions when they enter a zone.

 Suppose two people move into a zone, coming from opposite directions. ◦ Options for actions:  Each continues moving in the same direction.  Each turns around.  One turns around, other keeps moving the same way.  One can calculate the probability of both users doing a U-turn. ◦ Using the statistics in M, the probability of both doing a U-turn is 0.1 percent, while the probability of both going straight is 99.9 percent.

 The entropy in the aforementioned example is bits. ◦ Maximum entropy is a value of 1 bit.  When a hostile observer is able to observe the behavior of users over time the anonymity granted by mix zones and other anonymization methods greatly decreases.

 Half the battle is knowing how private and secure your information is.  Better methods of measuring location privacy allow users to make sound decisions about private data sharing.

 Managing application use of pseudonyms.  Reacting to insufficient anonymity.  Improving the models.  Dummy users.  Granularity.  Scalability.

Questions? Note: the link to this paper on the reading list is broken. Rather, you may download the full paper here: location.pdf location.pdf