Re: Anonymous Video on Demand

[jim@bilbo.suite.com (Jim Miller)]

could
As I think about it more, the "anonymous video on demand" problem can be  
solved with an oblivious transfer protocol.

Here's how I see it working:

(The following is adapted from the oblivious transfer protocol described  
in "Applied Cryptography" on page 98.)

Say Alice is the Video Vendor and Bob is the customer...

Alice generates a public/private key pair for each movie in her video  
database and publishes the public keys in an electronic catalog.  Each  
public key would be paired with a movie description and a catalog index  
number.

Bob downloads Alice's catalog and browses through it offline.  Bob makes a  
selection, and also randomly picks 99 (or any large number) other catalog  
numbers

Bob generates a random DES key and encrypts this key with the public key  
associated with his selection.

Bob sends the encrypted DES key and the list of 100 catalog numbers to  
Alice.

Alice decrypts the DES key with the private key associated each catalog  
number received from Bob.  In only one case will Alice successfully  
recover Bob's DES key, only she doesn't know which case.

Alice encrypts each movie selection with the resulting DES keys from the  
previous step and sends all 100 encrypted movies to Bob.

Bob will only be able to decrypt and view the movie he selected and Alice  
wont know which of the 100 movies Bob selected.

Ta Da!

The nice thing about this protocol is that it doesn't really have anything  
to do with videos.  It could be used for an electronic library, or any  
warehouse of digital information where the Vendor wishes to charge for a  
download, yet the Customer doesn't want the Vendor to know which item is  
selected.

Also, the Vendor still can use statistical analysis to determine which  
items are more popular and which items are less popular.  The Vendor could  
keep track of how many times an item was mentioned in a Customer selection  
list.  The more popular items would appear in more lists.  Unpopular items  
would have different statistics.  This analysis breaks down if all items  
are equally popular, or Customers don't chose the other 99 items randomly.


Jim_Miller@suite.com