Safely Measuring Tor. Rob Jansen U.S. Naval Research Laboratory Center for High Assurance Computer Systems
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1 Safely Measuring Tor Safely Measuring Tor, Rob Jansen and Aaron Johnson, In the Proceedings of the 23rd ACM Conference on Computer and Communication Security (CCS 2016). Rob Jansen Center for High Assurance Computer Systems 23 rd Conference on Computer and Communication Security Hofburg Imperial Palace, Vienna, Austria October 27 th, 2016
2 Talk Overview Estimated ~1.75 M. Users/Day (metrics.torproject.org) Tor: an anonymous communication, censorship resistant, privacy-enhancing communication system How is Tor being used? being misused? performing? PrivCount: A Distributed System for Safely Measuring Tor 2
3 Talk Overview Estimated ~1.75 M. Users/Day (metrics.torproject.org) Tor: an anonymous communication, censorship resistant, privacy-enhancing communication system How is Tor being used? being misused? performing? Objective: To safely gather Tor network usage statistics Approach: Use distributed measurement, secure multiparty computation, and differential privacy PrivCount: A Distributed System for Safely Measuring Tor 3
4 Background and Motivation How Tor works Why measurements are needed and what to measure Measurement challenges
5 Background: Onion Routing Users Circuit Relays Destinations PrivCount: A Distributed System for Safely Measuring Tor 5
6 Background: Onion Routing Users Circuit Stream Relays Destinations PrivCount: A Distributed System for Safely Measuring Tor 6
7 Background: Using Circuits PrivCount: A Distributed System for Safely Measuring Tor 7
8 Background: Using Circuits 1. Clients begin all circuits with a selected guard PrivCount: A Distributed System for Safely Measuring Tor 8
9 Background: Using Circuits 1. Clients begin all circuits with a selected guard 2. Relays define individual exit policies PrivCount: A Distributed System for Safely Measuring Tor 9
10 Background: Using Circuits 1. Clients begin all circuits with a selected guard 2. Relays define individual exit policies 3. Clients multiplex streams over a circuit PrivCount: A Distributed System for Safely Measuring Tor 10
11 Background: Using Circuits 1. Clients begin all circuits with a selected guard 2. Relays define individual exit policies 3. Clients multiplex streams over a circuit 4. New circuits replace existing ones periodically PrivCount: A Distributed System for Safely Measuring Tor 11
12 Background: Using Circuits 1. Clients begin all circuits with a selected guard 2. Relays define individual exit policies 3. Clients multiplex streams over a circuit 4. New circuits replace existing ones periodically 5. Clients randomly choose relays, weighted by bandwidth PrivCount: A Distributed System for Safely Measuring Tor 12
13 Background: Directory Authorities Directory Authorities Hourly network consensus by majority vote Relay info (IPs, pub keys, bandwidths, etc.) Parameters (performance thresholds, etc.) PrivCount: A Distributed System for Safely Measuring Tor 13
14 Motivation: Why Measure Tor? Why are Tor network measurements needed? To understand usage behaviors to focus effort and resources To understand network protocols and calibrate parameters To inform policy discussion PrivCount: A Distributed System for Safely Measuring Tor 14
15 Motivation: Why Measure Tor? Why are Tor network measurements needed? To understand usage behaviors to focus effort and resources To understand network protocols and calibrate parameters To inform policy discussion Tor metrics are the ammunition that lets Tor and other security advocates argue for a more private and secure Internet from a position of data, rather than just dogma or perspective. Bruce Schneier (June 1, 2016) (metrics.torproject.org) PrivCount: A Distributed System for Safely Measuring Tor 15
16 Motivation: Previous Measurement Studies Previous work collected, stored, and manually analyzed sensitive data McCoy et. al. (PETS 2008): tcpdump of first 150 bytes of packet (including 96 payload) Chaabane et. al. (NSS 2010): customized DPI software PrivCount: A Distributed System for Safely Measuring Tor 16
17 Motivation: Measurement Challenges Some Existing Measurements Data Published Privacy Techniques Unsafe Inaccurate Relay BW available Test measurements Relay BW used Aggregated ~ 4 hours Total # daily users Inferred (consensus fetches) # users per country Aggregated ~ 24 hours, rounded, opt-in Exit traffic per port Aggregated ~ 24 hours, opt-in PrivCount: A Distributed System for Safely Measuring Tor 17
18 Motivation: Measurement Challenges Some Existing Measurements Safety concerns: Per-relay outputs Data stored locally No privacy proofs Data Published Privacy Techniques Unsafe Inaccurate Relay BW available Test measurements Relay BW used Aggregated ~ 4 hours Total # daily users Inferred (consensus fetches) # users per country Aggregated ~ 24 hours, rounded, opt-in Exit traffic per port Aggregated ~ 24 hours, opt-in PrivCount: A Distributed System for Safely Measuring Tor 18
19 Motivation: Measurement Challenges Some Existing Measurements Accuracy concerns: Per-relay noise Opt-in, limited vantage points Data Published Privacy Techniques Unsafe Inaccurate Relay BW available Test measurements Relay BW used Aggregated ~ 4 hours Total # daily users Inferred (consensus fetches) # users per country Aggregated ~ 24 hours, rounded, opt-in Exit traffic per port Aggregated ~ 24 hours, opt-in PrivCount: A Distributed System for Safely Measuring Tor 19
20 Motivation: Missing Measurements Many useful statistics are not collected for safety Users Total number of unique users at any time, how long they stay online, how often they join and leave, usage behavior Relays Total bandwidth capacity, congestion and queuing delays, circuit and other failures, denial of service and other attacks Destinations Popular destinations, popular applications, effects of DNS, properties of traffic (bytes and connections per page, etc.) PrivCount: A Distributed System for Safely Measuring Tor 20
21 The PrivCount Measurement System PrivCount system architecture Distributed measurement and aggregation protocol Secure computation and private output
22 PrivCount: Overview Privacy-preserving counting system Consumes various new event types from Tor Circuit end events Stream end events Connection end events Counts various statistics from event information, e.g.: Total number of circuits, streams, connections Data volume per circuit, stream Number of unique users Based on PrivEx-S2 protocol of Elahi et. al. (CCS 2014) PrivCount: A Distributed System for Safely Measuring Tor 22
23 PrivCount: Overview Security goals for safer Tor measurements Forward privacy The adversary cannot learn the state of the measurement before time of compromise Differential privacy Prevents confirmation of the actions of a specific user given the output Secure aggregation Securely aggregates safe statistics across all measurement nodes Only the safe, aggregated measurement results are released PrivCount: A Distributed System for Safely Measuring Tor 23
24 PrivCount: Architecture Data Collectors (DCs) Collect events Increment counters DC1 DC2 PrivCount: A Distributed System for Safely Measuring Tor 24
25 PrivCount: Architecture Data Collectors (DCs) Collect events Increment counters DC1 DC2 Tally Server (TS) Central, untrusted proxy Collection facilitator TS PrivCount: A Distributed System for Safely Measuring Tor 25
26 PrivCount: Architecture Data Collectors (DCs) Collect events Increment counters DC1 DC2 Tally Server (TS) Central, untrusted proxy Collection facilitator TS Share Keepers (SKs) Stores DC secrets, sum for aggregation SK1 SK2 PrivCount: A Distributed System for Safely Measuring Tor 26
27 PrivCount: Initialization DC1 DC2 Create deployment document Privacy parameters ε and δ Sensitivity for each statistic (max change due to single client) Noise weight ω (relative noise added by each DC) TS Deploy SK1 SK2 PrivCount: A Distributed System for Safely Measuring Tor 27
28 PrivCount: Initialization Deploy DC1 DC2 Deploy Send to all DCs and SKs DCs and SKs accept only on unanimous consensus TS SK1 SK2 Deploy Deploy PrivCount: A Distributed System for Safely Measuring Tor 28
29 PrivCount: Configuration DC1 DC2 Create configuration document Collection start and end times Statistics to collect Estimated value for each statistic (maximize relative per-statistic accuracy while providing (ε, δ)- differential privacy) SK1 TS SK2 Config PrivCount: A Distributed System for Safely Measuring Tor 29
30 PrivCount: Configuration Config DC1 DC2 Config Send to all DCs and SKs DCs and SKs check for consistency TS SK1 SK2 Config Config PrivCount: A Distributed System for Safely Measuring Tor 30
31 PrivCount: Execution - Setup DC1 DC2 N_DC1 N_DC2 Generate noise for each counter N ~ Normal(0,ωσ) mod q Contributes to differential privacy of the outputs TS SK1 SK2 PrivCount: A Distributed System for Safely Measuring Tor 31
32 PrivCount: Execution - Setup DC1 DC2 N_DC1 N_DC2 Generate random share for each SK S ~ Uniform({0,, q-1}) Blinds the actual counts for forward privacy at the DCs TS SK1 SK2 PrivCount: A Distributed System for Safely Measuring Tor 32
33 PrivCount: Execution - Setup DC1 DC2 N_DC1 N_DC2 DCs send shares to SKs TS SK1 SK2 PrivCount: A Distributed System for Safely Measuring Tor 33
34 PrivCount: Collection C_DC1 C_DC2 DC1 DC2 N_DC1 N_DC2 DCs collect events and increment counters TS SK1 SK2 PrivCount: A Distributed System for Safely Measuring Tor 34
35 PrivCount: Collection C_DC1 C_DC2 DC1 DC2 N_DC1 N_DC2 Send Counters to TS TS SK1 SK2 PrivCount: A Distributed System for Safely Measuring Tor 35
36 PrivCount: Aggregation DC1 DC2 TS combines all counter values from DCs and SKs Subtracts SK-held values from DC-held values TS C_DC1 N_DC1 + C_DC2 N_DC2 - - SK1 SK2 PrivCount: A Distributed System for Safely Measuring Tor 36
37 PrivCount: Aggregation DC1 DC2 TS C_DC1 C_DC2 TS combines all counter + values from DCs and SKs Subtracts SK-held values from DC-held values - N_DC1 N_DC2 - SK1 SK2 PrivCount: A Distributed System for Safely Measuring Tor 37
38 PrivCount: Aggregation DC1 DC2 Results are differentially private and safe to publish TS C_DC1 C_DC2 SK1 SK2 N_DC1 N_DC2 PrivCount: A Distributed System for Safely Measuring Tor 38
39 PrivCount: Security Recall: Security Properties Forward privacy The adversary cannot learn the state of the measurement before time of compromise Differential privacy Prevents confirmation of the actions of a specific user given the output Secure aggregation Securely aggregates safe statistics across all measurement nodes Only the safe, aggregated measurement results are released PrivCount: A Distributed System for Safely Measuring Tor 39
40 PrivCount: Security C_DC1 C_DC2 DC1 DC2 N_DC1 N_DC2 TS C_DC1 N_DC1 + C_DC2 N_DC2 - - SK1 SK2 PrivCount: A Distributed System for Safely Measuring Tor 40
41 PrivCount: Security C_DC1 C_DC2 N_DC1 DC1 DC2 N_DC2 SK1 TS Forward Privacy Nothing learned from counter before time of compromise as long as 1 SK is honest N_DC1 N_DC2 SK2 C_DC1 + C_DC2 - - PrivCount: A Distributed System for Safely Measuring Tor 41
42 PrivCount: Security C_DC1 C_DC2 DC1 DC2 N_DC1 N_DC2 Differential Privacy Enough noise is added as long as a tunable subset of DCs are honest TS C_DC1 N_DC1 + C_DC2 N_DC2 - - SK1 SK2 PrivCount: A Distributed System for Safely Measuring Tor 42
43 PrivCount: Security C_DC1 C_DC2 DC1 DC2 N_DC1 N_DC2 Secure Aggregation Count+noise is added securely the TS only learns the aggregated sum TS C_DC1 N_DC1 + C_DC2 N_DC2 - - SK1 SK2 PrivCount: A Distributed System for Safely Measuring Tor 43
44 PrivCount: Security C_DC1 C_DC2 DC1 DC2 N_DC1 N_DC2 See paper for more details and for security and privacy proofs TS C_DC1 N_DC1 + C_DC2 N_DC2 - - SK1 SK2 PrivCount: A Distributed System for Safely Measuring Tor 44
45 Deployment and Measurement Results Configuring and running Tor relays Exploratory measurements using various exit policies In-depth measurements of most popular usage Network-wide measurement inference
46 Deploying PrivCount DCs 3 entry relay data collectors 0.16% entry bandwidth 1 TS and 6 SKs from 6 operators and 4 countries TS 4 exit relay data collectors 1.10% exit bandwidth SKs PrivCount: A Distributed System for Safely Measuring Tor 46
47 Collection Phases Exploratory phases Explore various exit policies (strict, default, open) Explore various applications (web, interactive, other) Gather only totals (circuits, streams, bytes) Use Tor metrics to estimate input parameters Run for 1 day, iterate In-depth phases Focus on most popular exit policy and applications Gather totals and histograms Use exploratory results to estimate input parameters Run for 4 days for client stats, 21 days for exit stats PrivCount: A Distributed System for Safely Measuring Tor 47
48 Results: Exit Policies Number of Circuits Traffic by Exit Policy 10 6 Default Open Strict Number of Streams Default Open Strict Open file sharing ports reduce web data transferred Interactive Web Other Data Transferred (GiB) Default Open Strict PrivCount: A Distributed System for Safely Measuring Tor 48
49 Results: Amount and Types of Traffic Connections (%) Interactive Web Other Bytes (%) Increase in web traffic 42% in 2010 to 91% in [1] PETS 2008, McCoy... [2] NSS 2010, Chaabane...[3] CCS 2016, Jansen... PrivCount: A Distributed System for Safely Measuring Tor 49
50 Results: Number of Unique Users Unique Users (10 Minutes) Total Active Inactive Active 710k total users 550k (77%) active users In an average 10 mins. PrivCount: A Distributed System for Safely Measuring Tor 50
51 Results: Number of Unique Users Unique Users (10 Minutes) Total Active Inactive Active 710k total users 550k (77%) active users In an average 10 mins. ~800k ~1.6m average concurrent users (Tor Browser update pings webstats2/) PrivCount: A Distributed System for Safely Measuring Tor 51
52 Results: Number of Unique Users Unique Users (10 Minutes) Total Active Inactive Active 710k total users 550k (77%) active users In an average 10 mins. ~800k ~1.6m average concurrent users (Tor Browser update pings webstats2/) ~1.75m daily users (Consensus downloads PrivCount: A Distributed System for Safely Measuring Tor 52
53 Results: Traffic Modeling Statistics More results in the paper! PrivCount: A Distributed System for Safely Measuring Tor 53
54 Conclusion PrivCount Distributed measurement system using secret sharing Safer Tor measurement study Open source: Future measurement plans Network traffic to create realistic traffic models Onion services to improve reliability and scalability Better techniques for cardinality (e.g., # unique users) Detecting denial of service attacks and other misbehavior Contact rob.g.jansen@nrl.navy.mil, PrivCount: A Distributed System for Safely Measuring Tor 54
55 Questions
56 PrivCount vs PrivEx How does PrivCount enhance PrivEx Multi-phase iterative measurement Expanded privacy notion that simultaneously handles multiple types of measurements Optimal allocation of the ε privacy budget across multiple statistics Composable security definition and proof More capable and reliable tool Supports over 30 different types of Tor statistics Resilience to node failures and reboots Simpler configuration and setup PrivCount: A Distributed System for Safely Measuring Tor 56
57 Privacy Parameters for (ε, δ)-differential privacy ε = 0.3 : same as used by Tor onion service stats δ = 10-3 : upper bound on prob. of choosing noise value that violates ε-differential privacy DCs on 3 machines, add 3x noise User action bounds Action Simultaneous open entry connections 1 Entry connection open time New entry connections 12 New circuits 146 Bound 24 hours New streams 30,000 Data sent or received 10 MiB PrivCount: A Distributed System for Safely Measuring Tor 57
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