private matchings and allocations
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Private Matchings and Allocations
Joint work with
Justin Hsu (Penn)Zhiyi Huang (HKU)Aaron Roth (Penn)
Tim Roughgarden (Stanford)
Speaker: Steven WuUniversity of Pennsylvania
STOC 2014
The Allocation Problem
nbidders
mitems
An important special case:
goods agents
1
2
3
A
B
C
• Unit demand valuations
• Equivalent to max-weight matching
Our GoalHigh social welfare allocation
Privacy(without revealing individual private valuations)
D
Differential Privacy
Algorithm
ratio bounded
AliceAlice BobBob ChrisChris DonnaDonna ErnieErnieXavierXavier
Differential Privacy
• An algorithm A with domain X and range R satisfies ε-differential privacy if for every outcome r and every pair of databases D, D’ differing in one record:
Pr[ A(D) = r ] ≤ (1 + ε)Pr[ A(D’) = r]
• Domain: Reported valuation functions• Range: Matchings
Problem: Assignment Reveals
Preference
• Problem: High welfare matching will give people what they want.
Separate Outputs
Algorithm
Protect from Coalition
Algorithm!
Joint Differential Privacy
(KPRU’14)
(MM09, GLMRT10)
Supply Assumption• We need multiple copies for each
type of good even under JDP.• How many?
Impossible Trivial
Main ResultTheorem: There is a JDP algorithm in the that solves the max-weight matching problem with n people and k types of goods with supply at least s each, and outputs a matching of weight
OPT – αn
whenever:
A Framework for JDP
The “Billboard Model”
“Low information” Signalo From the signal, every bidder can figure out what
item they are matched to in a matching
o Does not reveal each individual’s private data
• Think: Prices
Max Matchings (A Sketch)A remarkable algorithm for Max-Matchings: [Kelso and Crawford ’82]
0.5 0.1
0 0.2
$0$0
$0.1
$0.2
Outbid
$0.1
Bid Again
Welfare
Prices as informationClaim: Bidders just need to see the prices
1. Prices are sufficient to identify the favorite good
2. When price raises again, a bidder is unmatched
3. Bidders are matched to the last thing they bid on
• Just need to count how many bids each good received!
Privately Maintaining Counts
10011101032
• Private (noisy) counters under continual observation [DNPR10, CSS10]
• Given a stream of T bits, maintain an estimate of the running count with accuracy
o Single Stream of sensitivity 1
Privately Maintaining Counts
1 1 11110001810011101032000111110192
• A straightforward generalization:K counters on K streams that collectively have sensitivity Δ gives accuracy
Lower SensitivityStopping the auction early
with a new condition
• Sensitivity
Counter Error
• Error per bid counter
Supply
• Goods might also be under/over-allocated by E.o Doesn’t reduce the welfare by more than (1-α) factor if
Main TheoremTheorem: There is a private algorithm in the billboard model that solves the max-weight matching problem with n people and k types of goods with supply at least s each, and outputs a matching of weight
OPT – αn
whenever:
Extensions• Results extend to the allocation
problem when buyers have gross substitute preferences.
Conclusions• Some problems that can’t be solved under DP
can be solved under joint-DP.o If the output is partitioned among the agentso The agent’s output is allowed to be sensitive in his input.
• Billboard model: interesting framework to design a joint-DP algorithm?
Private Matchings and Allocations
Joint work with
Justin Hsu (Penn)Zhiyi Huang (HKU)Aaron Roth (Penn)
Tim Roughgarden (Stanford)
Speaker: Steven WuUniversity of Pennsylvania
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