1. Measuring and Monitoring the Tor Network Aaron Johnson
August 19th, 2018
Encryption and Surveillance Workshop

2. References and Acknowledgements
Understanding Tor Usage with Privacy-Preserving Measurement
Akshaya Mani (Georgetown University),
T Wilson-Brown (UNSW Canberra Cyber, University of New South Wales),
Rob Jansen (U.S. Naval Research Laboratory)
Aaron Johnson (U.S. Naval Research Laboratory)
Micah Sherr (Georgetown University),
To appear in the 2018 Internet Measurement Conference.
Tunable Transparency: Secure Computation in the Tor Network
Ryan Wails (U.S. Naval Research Laboratory)
Aaron Johnson (U.S. Naval Research Laboratory)
Daniel Starin (George Mason University, Vencore Labs)
Arkady Yerukhimovich (MIT Lincoln Laboratory)
S. Dov Gordon (George Mason University)
In preparation (draft available).

3. Background: Tor

4. Destinations Users Tor Background
Tor is a popular system for anonymous, censorship-resistant Internet communication.

5. Tor Background: Onion Routing
Users
Relays
Destinations
Circuit
Stream

6. Tor Background: Onion Routing
Users
Relays
Onion Services
(e.g. nytimes3xbfgragh.onion)
Circuit
Stream

7. Tor Background: Who Uses Tor
Over 2,000,000 daily users
Over 6000 relays in over 75 countries
100Gbps aggregate traffic

8. Tor Measurement and Monitoring
Do network privacy and transparency conflict?

9. Problem: Privacy & Transparency

10. Tor Measurement and Monitoring
Privacy risks of measuring Tor
Deanonymizing individual connections
Storing sensitive data at relays risks leaks from compromise
Revealing “interesting” users (e.g. from censored locations)
Revealing private onion services

11. Tor Measurement and Monitoring
Problems without some transparency
Level of anonymity unknown
Network subject to silent attack and abuse
Network can be covertly used for attack and abuse
Network management and improvement difficult

12. Some current Tor measurements
Some current Tor measurements
Data
How measured
Privacy techniques
Relay bandwidth capacity
Self, BW Authorities
Test measurements
Relay used bandwidth
Per relay
Report every 4 hrs
Total daily users
Inferred from consensus downloads
Users per country
Report every 24 hrs,
round, opt-in
# onion services
Differential privacy, round
Exit traffic per port
Report every 24 hrs, opt-in

13. Some current Tor measurements
Some current Tor measurements
Data
How measured
Privacy techniques
Relay bandwidth capacity
Self, BW Authorities
Test measurements
Relay used bandwidth
Per relay
Report every 4 hrs
Total daily users
Inferred from consensus downloads
Users per country
Report every 24 hrs,
round, opt-in
# onion services
Differential privacy, round
Exit traffic per port
Report every 24 hrs, opt-in
Inaccurate

14. Some current Tor measurements
Some current Tor measurements
Data
How measured
Privacy techniques
Relay bandwidth capacity
Self, BW Authorities
Test measurements
Relay used bandwidth
Per relay
Report every 4 hrs
Total daily users
Inferred from consensus downloads
Users per country
Report every 24 hrs,
round, opt-in
# onion services
Differential privacy, round
Exit traffic per port
Report every 24 hrs, opt-in
Unsafe

15. Some current Tor measurements
Some current Tor measurements
Data
How measured
Privacy techniques
Relay bandwidth capacity
Self, BW Authorities
Test measurements
Relay used bandwidth
Per relay
Report every 4 hrs
Total daily users
Inferred from consensus downloads
Users per country
Report every 24 hrs,
round, opt-in
# onion services
Differential privacy, round
Exit traffic per port
Report every 24 hrs, opt-in
Incomplete

16. Secure Aggregation

17. Secure Aggregation Data Collection: Developed two systems:
Data Collectors
(DCs) / Relays x1 x2 x3
Output is noisy aggregate, hiding the inputs xi.
Data Aggregators (DAs) m
Data Collection:
DCs store data obliviously during measurement period.
DCs secret-share inputs to DAs at end of measurement period.
DAs run protocol to aggregate and add differentially-private noise.
Developed two systems:
PrivCount: Computes sums
PSC: Computes private set-union cardinality
Tolerate m-1 malicious DAs
Transitioning PrivCount into Tor: Proposal 288

18. Tor Measurement Study Performed Tor measurements
Exit, entries, and onion-service statistics
24-hour measurements
January – May 2018
Ran 16 Tor relays
1.5% total exit, 1.2% guard, 2.8% onion lookup
Canada, France, US
Used PrivCount and PSC
3 Data Aggregators (DAs)
3 DA operators
Located in US and Australia

19. Tor Measurement Study: Exit Statistics
Tor Web connections to popular domains (Alexa top 1M)

20. Tor Measurement Study: Entry and Onion Services
Daily client activity (95% CI inferred network-wide)
Unique client IPs: 6.61 – 11.2 million
“Promiscuous” clients: 14,400 – 21,500
Daily onion-service activity (95% CI inferred network-wide)
1,350 – 1,740 lookups/second
1,192 – 1,620 failed lookups/s
~93% failure rate

21. Secure Multiparty Computation

22. Secure Multiparty Computation
Flexible transparency with MPC
Robust statistics to limit effect of malicious
Improved client-size estimation
Measure abuse of and with Tor
Botnets on onion services
Denial-of-service attacks
Hacking attempts (e.g. vulnerability scanning)
Site scraping

23. Secure Multiparty Computation
Data Collectors
(DCs) / Relays x1 x2 x3
Output is some function f(x1,x2,x3), hiding the inputs xi.
Computation Parties (CPs) m
Data Collection:
DCs store data obliviously during measurement period.
DCs secret-share inputs to CPs at end of measurement period.
CPs run protocol to compute some function f on the inputs.
Tor MPC design
TinyOT (Burra et al. 2015) for offline/online Boolean-circuit evaluation.
Secure against malicious, dishonest majority.

24. Secure Multiparty Computation
TinyOT performance estimates
7,000 Data Collectors
5 Computation Parties
40-bit statistical security
Median
Count Distinct
Offline communication
12.7 GB
31.43 GB
Offline time (1Gbps BW)
1.69 minutes
4.19 minutes
Offline throughput
852/day
344/day
Online time (200ms RTT)
5 minutes
2 seconds
off
- in: sb
- tri: 9*4(m-1)sL on
- in: b
- tri: 2L
s = 40
n = 7000
m = 5
c = 1 (bandwidth in Gbps)
t = 0.1 (one-way latency in seconds)
Med:
- in: 224,000
- AND: 17,600,000
- AND depth: 3,003
- Total offline comm: (40*(224_000) + 9*4*4*40*(17_600_000)) = Gb = GB
- Total offline time: seconds = 1.69 minutes
- Throughput: / day
- Total online time: 3_003 * 0.1 = seconds = 5.01 minutes
M = 5
Error: 5.8%
Log:
- in: 6,160,000,000
- AND: 870,000
- AND depth: 20
- Total offline comm: (40 * 6_160_000_ *4*4*40*870_000) = Gb = GB
- Total online time: seconds = 4.19 minutes
- Throughput: / day
- Total online time: 20 * 0.1 = 2.0 seconds
32-bit median values, count-distinct error 5.8% (LogLog)

25. Conclusions
Tor is developing privacy-focused mechanisms for measurement and monitoring.
Flexible transparency mechanisms raise new issues
If Tor can reveal information, will it become obligated to do so?
Where should the line between transparency and privacy be drawn?
What governance mechanisms can handle making these decisions?
Other systems may face similar measurement questions
Privacy-enhanced cryptocurrencies (Zcash, Monero)
Privacy-enhanced cloud services