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mitigated by combining work from recent studies: e.g., by checking whether an enclave code leaks secrets [65]; by protecting against side-channel attacks [62, 64]; or by leveraging software fault isolation [38, 62]). 8 Related Work Software for trusted execution environments: Various TEEs such as TPM, ARM TrustZone, and AMD SVM have been used for guaranteeing the security of applications in mobile and PC environments. Since the traditional trusted computing technologies (e.g., hypervisor-based approach with TPM) rely on the chain of trust, it makes the size of TCB larger. Flicker [52] proposed an approach that executes only a small piece of code inside the trusted container, where it extremely reduces the TCB. Nevertheless, it suffers from performance limitations. Intel SGX removes this challenge by offering native performance and multi-threading. In addition, the cloud computing and hosting service providers, where Tor relays are often hosted [14], is predominantly x86-based. Applications for Intel SGX: Haven [26] pioneered adopting Intel SGX in the cloud environment with an unmodified application. VC3 [61] proposed data analytics combined with Intel SGX in the cloud. Moat [65] studied the verification of application source code to determine whether the program actually does not leak private data on top of Intel SGX. Kim et al. [46] explores how to leverage SGX to enhance the security and privacy of network applications. These studies are early studies of SGX that rely on SGX emulators [42]. S-NFV [63] applies SGX to NFV to isolate its state from the NFV infrastructure and platform and presents preliminary performance evaluations on real SGX hardware. In contrast, we show how SGX can improve the trust model and operation of Tor and SGX-Tor run on real SGX hardware. Attacks and security analysis on Tor: §3 discussed many attacks on Tor. SGX-Tor prevents modification of Tor binaries and limits attackers’ ability; attackers can still launch attacks within the network outside Tor nodes. For example, attackers can still mount traffic analysis attacks or website finger-printing attacks. While Tor does not try to protect against traffic confirmation attacks [35], it aims to protect against general traffic analysis attacks. In particular, large-scale traffic correlation [29] and website finger-printing [45] attacks are believed to be very difficult in practice [22, 45] because those attacks require achieving an arbitrarily low false positive rate as the number of users becomes larger. Security analysis of Tor on a realistic workload is an ongoing research [34] area. In this work, we show how we can thwart known attacks against the Tor ecosystem by using a commodity trusted execution environment, Intel SGX. 9 Conclusion Due to the wide adoption of the x86 architecture, Intel Software Guard Extensions (SGX) potentially has a tremendous impact on providing security and privacy for network applications. This paper explores new opportunities to enhance the security and privacy of a Tor anonymity network. Applying SGX to Tor has several benefits. First, we show that deploying SGX on Tor can defend against known attacks that manipulate Tor components. Second, it limits the information obtained by running or compromising Tor components, reducing the power of adversaries to network-level adversaries, who can only launch attacks external to the Tor components. Finally, this brings changes to the trust model of Tor, which potentially simplifies Tor operation and deployment. Our extensive evaluation of the SGX-Tor shows that SGX-enabled Tor components incur small performance degradation and supports incremental deployment on the existing Tor network, demonstrating its viability. 10 Acknowledgment We thank the anonymous reviewers and our shepherd Phillipa Gill for their feedback. We also thank Will Scott, Chris Magistrado, Hyeontaek Lim, and Inho Choi for their comments on earlier versions. Dongsu Han is the corresponding author. This work was supported in part by the IITP funded by the Korea government (MSIP) [B0101- 16-1368, Development of an NFV-inspired networked switch and an operating system for multi-middlebox services] and [B0190-16-2011, Korea-US Collaborative Research on SDN/NFV Security/Network Management and Testbed Build]; Office of Naval Research Global (ONRG); and NSF awards DGE-1500084, CNS-1563848, and CRI- 1629851. References [1] Alexa: The top 500 sites on the web. http:// www.alexa.com/topsites. [2] Amazon Web Service. https://aws.amazon. com/. [3] Atlas, flag:authority. https://atlas. torproject.org/#search/flag: authority. [4] CollecTor. https://collector. torproject.org/. [5] FBI’s use of Tor exploit is like peering through “broken blinds”. http://arstechnica. com/tech-policy/2016/06/fbis-useof-tor-exploit-is-like-peeringthrough-broken-blinds/. Last accessed: Aug 2016. USENIX Association 14th USENIX Symposium on Networked Systems Design and <strong>Implementation</strong> 157
[6] Intel Software Guard Extensions (Intel SGX) SDK. https://software.intel.com/enus/sgx-sdk. Last accessed: Aug 2016. [7] libevent-2.0.22. http://libevent.org/. [8] OpenSSL-1.1.0. https://www.openssl. org/. [9] Sgx virtualization. https://01.org/intelsoftware-guard-extensions/sgxvirtualization. [10] Tor bandwidth scanner. https://trac. torproject.org/projects/tor/ attachment/ticket/2861/bwauthspec.txt. [11] Tor: Bridges. https://www.torproject. org/docs/bridges.html.en. Last accessed: May, 2016. [12] Tor Browser. https://www.torproject. org/projects/torbrowser.html.en. [13] Tor Metrics. https://metrics. torproject.org/. [14] Tor Project: GodBadISPs. https: //trac.torproject.org/projects/ tor/wiki/doc/GoodBadISPs. [15] Tor:Overview. https://www.torproject. org/about/overview. Last accessed: Aug 2016. [16] zlib-1.2.8. http://www.zlib.net/. [17] Research problem: measuring the safety of the tor network. https://blog.torproject.org/ blog/research-problem-measuringsafety-tor-network, 2011. [18] Research problems: Ten ways to discover Tor bridges. https://blog.torproject.org/ blog/research-problems-ten-waysdiscover-tor-bridges, Oct 2011. [19] Extend OOM handler to cover channels/connection buffers. https://trac.torproject.org/ projects/tor/ticket/10169, 2014. [20] How to report bad relays. https: //blog.torproject.org/blog/howreport-bad-relays, July 2014. [21] Tor security advisory: ”relay early” traffic confirmation attack. https://blog.torproject. org/blog/tor-security-advisoryrelay-early-traffic-confirmationattack, July 2014. [22] Traffic correlation using netflows. https: //blog.torproject.org/category/ tags/traffic-confirmation, Nov 2014. [23] The chutney tool for testing and automating Tor network setup. https://gitweb. torproject.org/chutney.git, 2015. Accessed: 09/01/2016. [24] I. Anati, S. Gueron, S. P. Johnson, and V. R. Scarlata. Innovative Technology for CPU Based Attestation and Sealing. In Proc. HASP, pages 1–8, Tel-Aviv, Israel, 2013. [25] K. Bauer, D. McCoy, D. Grunwald, T. Kohno, and D. Sicker. Low-resource Routing Attacks Against Tor. In Proc. ACM Workshop on Privacy in Electronic Society, 2007. [26] A. Baumann, M. Peinado, and G. Hunt. Shielding Applications from an Untrusted Cloud with Haven. In Proc. USENIX OSDI, 2014. [27] A. Biryukov, I. Pustogarov, and R.-P. Weinmann. Trawling for Tor Hidden Services: Detection, Measurement, Deanonymization. In Proc. IEEE Symposium on Security and Privacy, pages 80–94, 2013. [28] N. Borisov, G. Danezis, P. Mittal, and P. Tabriz. Denial of Service or Denial of Security? In Proc. ACM CCS, 2007. [29] S. Chakravarty, M. V. Barbera, G. Portokalidis, M. Polychronakis, and A. D. Keromytis. On the effectiveness of traffic analysis against anonymity networks using flow records. In Proc. Passive and Active Measurement, 2014. [30] S. Checkoway and H. Shacham. Iago Attacks: Why the System Call API is a Bad Untrusted RPC Interface. In Proc. ASPLOS, 2013. [31] V. Costan and S. Devadas. Intel sgx explained. Technical report, Cryptology ePrint Archive, Report 2016/086, 2016. https://eprint. iacr. org/2016/086. [32] R. Dingledine. Tor Project infrastructure updates in response to security breach. http: //archives.seul.org/or/talk/Jan- 2010/msg00161.html, January 2010. [33] R. Dingledine. Turning funding into more exit relays. https://blog.torproject.org/ blog/turning-funding-more-exitrelays, July 2012. [34] R. Dingledine. Improving Tor’s anonymity by changing guard parameters. https://blog. torproject.org/blog/improvingtors-anonymity-changing-guardparameters, Oct 2013. [35] R. Dingledine, N. Mathewson, and P. Syverson. Tor: The Second-generation Onion Router. In Proc. USENIX Security Symposium, 2004. 158 14th USENIX Symposium on Networked Systems Design and <strong>Implementation</strong> USENIX Association
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conference proceedings ISBN 978-1-9
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Proceedings of NSDI ’17: 14th USE
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NSDI ’17: 14th USENIX Symposium o
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Data-Driven Systems Encoding, Fast
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Message from the NSDI ’17 Program
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• Security. Before reading input
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3.3 Details of Lepton’s probabili
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5 Deployment at Scale To deploy Lep
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p50/p75/p95/p99 time (s) 1.6 1.4 1.
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0.7 0.6 0.5 0.4 0.3 0.2 0.1 0.0 21:
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configuration change, it took two h
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? ? Figure 16: An entire row (or co
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Decibel: Isolation and Sharing in D
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Single dVol instance: Per-core runt
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3K\VLFDO%ORFN$GGUHVVELWV : , ' / EL
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Throughput (K IOPS) Throughput (K I
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are admitted. The dVol metadata is
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Throughput (K IOPS) Latency (us) 18
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not at the cost of the throughput o
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ceedings of the 9th USENIX Conferen
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[57] SCHROEDER, B., LAGISETTY, R.,
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materialized streams. Unlike stream
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"sequencers": 10.0.0.1, "segments":
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class User { String name; String pa
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class CSMRMap implements Map { fina
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ack to using backpointers. Since th
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● ● ● ● ● ● ● ● ●
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[20] Robert Escriva, Bernard Wong,
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Curator: Self-Managing Storage for
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Figure 1: Cluster architecture and
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finds all the extent groups that ha
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Fraction of Clusters 1 0.8 0.6 0.4
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decisions on where to put a piece o
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use negative latencies, 18 i.e., th
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References [1] Q-learning with Neur
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D ML-driven Policies Workloads We u
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Abstract Evaluating the Power of Fl
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cannot guarantee that forwarded pac
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Estimate Error (%) 100 80 60 40 20
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since the last update as t ∆ . Be
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CDF 1 0.8 0.6 RCP 0.4 RCP-Approx XC
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perform RCP for long flows and perf
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References [1] AL-FARES, M., RADHAK
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A Building Blocks and their Use in
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APUNet: Revitalizing GPU as Packet
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Excavator CPU L1 Cache L2 Cache GCN
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CPU GPU RAM CPU GPU RAM OS CPU / Di
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W RQ CPU Master M Data Queue Worker
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Packet latency (us) 18 15 12 9 6 3
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Throughput (Gbps) 20 15 10 5 0 CPU
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References [1] Amazon. https://www.
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Stateless Network Functions: Breaki
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2 Motivation Simply running virtual
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Network Function State Key Value Ac
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network-wide capabilities, and sing
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create or destroy network functions
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Page 120 and 121: References [1] Algo-logic systems.
Page 122 and 123: [43] R. Potharaju and N. Jain. Demy
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Page 128 and 129: To detect the free-riding attack, w
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Page 136 and 137: 8 Acknowledgments We would like to
Page 138 and 139: [56] S. Peter, J. Li, I. Zhang, D.
Page 140: Appendix B 1 // count # of packets
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Page 157 and 158: microarchitectures. We ask ourselve
Page 159 and 160: ing, generating certificates, and c
Page 161 and 162: User process (Tor application) Tor-
Page 163 and 164: are SGX-enabled. However, we also s
Page 165 and 166: Data structure Tor Network-level ad
Page 167: Bandwidth (KB/s) 600 Probability (%
Page 171 and 172: [61] F. Schuster, M. Costa, C. Four
Page 174 and 175: ViewMap: Sharing Private In-Vehicle
Page 176 and 177: samples. Time-series analysis on lo
Page 178 and 179: time t 1 time t 60 A B VP v B VP x
Page 180 and 181: Platform Blur time I/O time Frame r
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Page 184 and 185: Vehicle 1 Vehicle 2 Vehicle 1 Vehic
Page 186 and 187: References [1] Top Rider. 2015. Res
Page 188 and 189: A System to Verify Network Behavior
Page 190 and 191: OpenSSL and BoringSSL. Verification
Page 192 and 193: PQ PT PR ! "#$ 1-:$'HF7$5 PS Figure
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Page 198 and 199: ●●●●●●● ● ●●●
Page 200 and 201: References [1] B. Anderson, S. Paul
Page 202 and 203: scheduler thread and one or more wo
Page 204 and 205: DEFINE_MODEL (void , AES_encrypt ,
Page 206: int main () { unsigned int x, y; MA
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throughput gains for the same numbe
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Units. European Wireless Conference
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Facilitating Robust 60 GHz Network
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AoD Tx array Reflecting point AoA R
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Algorithm 1 Virtual Beamforming 1:
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Reflector Reflector Rx’’’ Rx
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True AoA/AoD (deg.) 360 300 240 180
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360 300 LOS 0 -5 240 -10 180 -15 12
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12. References [1] B. Wire, “FCC
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Skip-Correlation for Multi-Power Wi
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Figure 4: Example showing need for
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on a basic property of correlation-
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Figure 9: Running sum block in Schm
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A B B can sense A +3dB a) Experimen
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Probability 10 0 10 -1 η + 10 -2
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References [1] Aironet 1570 Series
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µ(C) = { LE 2 N /2 (LE S ) 2 /4 +
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FM Backscatter: Enabling Connected
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ter techniques described above and
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signal shown in Fig. 3. If we norma
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four FM stations. These plots confi
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Bit-error rate 0.35 0.3 0.25 0.2 0.
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PESQ score -20 dBm -30 dBm -40 dBm
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(a) (b) Figure 15: Talking Poster a
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[16] Silicon labs si4713-b30-gmr. h
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Prio: Private, Robust, and Scalable
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Privacy. Prio provides f -privacy,
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4.2 Constructing SNIPs To run the S
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the servers can execute the verific
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Workstation Phone Field size: 87-bi
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Client time (s) Lower is better. 10
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(a) RAPPOR [57] provides differenti
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[27] Brickell, J., and Shmatikov, V
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[78] Janosi, A., Steinbrunn, W., Pf
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there exists an efficient simulator
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arithmetic confirm that σ i is a s
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where y i is the true value associa
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Opaque: An Oblivious and Encrypted
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other process on the same processor
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Note that Opaque protects most cons
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all records that have the same grou
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SQL Query Opaque Logical Plan SELEC
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Patient SELECT FROM WHERE ⨝ ⨝ D
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Normalized runtime 10 2 10 1 10 0 1
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References [1] AMPlab, University o
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Splinter: Practical Private Queries
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answer = r1 + r2 ItemId Price ItemI
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true on an interval of the range of
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putation time is O(nlogn) and the c
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Dataset Query Desc. FSS Scheme Inpu
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Figure 12: Per-core throughput of v
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Acknowledgements We thank our anony
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[55] R. J. Rosen. Study: Consumers
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or desirable. Instead, we created a
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IGMP Snooping [3], that we don’t
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On the inbound direction, the layer
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updated to take as input a FlowID a
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VFP from the stack, uninstall VFP (
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however, that our controllers neede
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[22] Overview of Single Root I/O Vi
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switch sends a notification to the
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messages in bounded time - this is
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Control Application Controller Prox
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Network Event Controller 1 Aware Aw
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1 0.8 SCL Consensus 1 0.8 SCL Conse
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CDF 1 0.8 0.6 0.4 SCL Consensus Tra
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References [1] A. Akella. Experimen
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a single path to ensure safety poli
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Robust validation of network design
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not discuss this extensively, our f
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(F)max v,λ,x ∑ t,i≠t d it (v i
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The robust optimization literature
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Maximum Link Utilization (MLU) 1.50
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Maximum Link Utilization (MLU) 3.00
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A Appendix Proof of Proposition 1:
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Experience with a globally-deployed
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Encoding, Fast and Slow: Low-Latenc
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At the time of writing, AWS Lambda
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computational power (TFLOPS) 10 9 8
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initializes the decoder’s state:
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threads start 25 s 50 s 75 s 100 s
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average bitrate (Mbit/s) 20 25 30 3
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er 9, 2015). http://techblog.netfli
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Live Video Analytics at Scale with
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1 " name ": " LicensePlate ", 2 " t
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Deep Neural Network (DNN) Classifie
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6 U Q 3 query 1 query 2 0 0 Q M 1 0
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We first distribute the query resou
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0.95 0.90 0.85 0.80 Quality 1.00 La
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Scheduling Time (s) 6 5 4 3 2 Numbe
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[38] G. Cormode, M. Garofalakis, P.
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Pytheas: Enabling Data-Driven Quali
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JoinTime (ms) 4000 3500 3000 2500 2
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Frontend Backend Frontend (a)$Logic
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6.1 Requirements The Pytheas system
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Control*decision* [Features,Decisio
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Throughput (Thousand RPS) 180 160 1
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[6] How a/b testing works. http://g
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Let it Flow: Resilient Asymmetric L
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Scheme Granularity Information need
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3 Design and Hardware Implementatio
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Probability 0.5 0.45 0.4 0.35 0.3 0
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FCT (ms) 15 10 ECMP CONGA Let
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(e.g. packet loss, increased delay)
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References [1] M. Al-Fares, A. Louk
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Flowtune: Flowlet Control for Datac
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utility 2.0 1.5 1.0 0.5 0.0 0 10 20
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lb lb lb lb fb fb fb fb lb src fb f
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Throughput (Gbps) 10.0 7.5 5.0 2.5
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DCTCP Flowtune pFabric sfqCoDel XCP
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over−allocation (Gbps) 200 150 10
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gathered statistics, and at any giv
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[18] GREENBERG, A., HAMILTON, J. R.
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programmed scheme (Figure 1). The r
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For simplicity of emulation, all ab
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3.5 Flexplane APIs Flexplane expose
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y two. We run DropTail both in Flex
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6 DCTCP 20 ● 8 Normalized FCT 5 4
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Throughput (Gbits/s) 125 100 75 50
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[14] W. Bai, L. Chen, K. Chen, D. H
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I Can’t Believe It’s Not Causal
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Buffered Replicated Writes 1200 100
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# c l i t s i s t h e c l i e n t
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T 1 : s(1) r(x) w(y=10) c(2) T 3 :
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dinator recovery [32, 64]. Upon det
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Goodput (million ops/s) 1.4 1.2 1 0
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[5] ADYA, A. Weak Consistency: A Ge
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[65] ZHANG, Y., POWER, R., ZHOU, S.
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CherryPick: Adaptively Unearthing t
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Cost model: The cloud charges users
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leaving the interval between ⃗x 1
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Figure 6: Architecture of CherryPic
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(a) Running cost (b) S
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Figure 13: CherryPick learns dimini
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References [1] Apache Spark the fas
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AdaptSize: Orchestrating the Hot Ob
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Varnish Nginx AdaptSize SIZE-OPT +9
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average and 75% higher in some case
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HK trace US trace Total Requests 45
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Throughput (Gbps) 20 10 0 AdaptSize
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Byte Hit Ratio 1.00 0.75 0.50 0.25
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Appendix A Proof of Theorem 1 The r
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[37] JELENKOVIĆ, P. R., AND RADOVA
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Bringing IoT to Sports Analytics Ma
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Figure 2: Ball instrumentation: (a)
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Angle (degrees) 50 0 -50 Groundtrut
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jectory (derived from ViCon). The p
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(For example, if the integer ambigu
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Cumulative Angle (degrees) 4000 300
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[2] adidas micoach smart ball. http
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FarmBeats: An IoT Platform for Data
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as shown in Table 1), farms do not
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γ Minimum Data-Gap Parameters O T
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Front side (a) Omni-directional dro
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4 7.5 60 3.5 7 59 3 6.5 58 2.5 6 57
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3 4 5 Video Length (min) (a) (b) (c
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REFERENCES [1] IEEE 802.11af: https
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sist in UAV path planning. On a hig
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hand can block the signal. Said dif
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User rotated her head AP SNR (in dB
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Leakage TX to RX(in dB) -50 -60 -70
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MoVR estimates φ AP . To estimate
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CDF 1 0.8 0.6 0.4 0.2 0 0 200 400 6
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10 No Mirror 30 8 Concluding Remark
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[44] SUR, S., VENKATESWARAN, V., ZH
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that exhibit high degree of collect
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time (s) 35 30 25 20 15 10 5 Lab us
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Figure 6: A heatmap of the collecti
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Figure 9: WebGaze architecture. 5.2
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Alternative # WebGaze # WebGaze # W
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8 Discussions There are several tec
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[27] GAZEPOINTER. Control mouse cur
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RAIL: A Case for Redundant Arrays o
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(a) 10Gbps multi-mode (b) 10Gbps si
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Figure 7: Price and reach of standa
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The speed of the above algorithm de
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Figure 10: Our 10G-SR testbed. We s
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(a) 10Gbps (b) 40Gbps Figure 14: To
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Acknowledgments We thank Keren Berg
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Today, data center operators chose
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Enabling Wide-spread Communications
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copies of data to be stored in diff
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40dB 30dB 20dB 0 5 10 15 # nodes on
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Figure 8: The OWS box we implemente
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Mbps Mbps 4×10 5 2×10 5 (a) 0 0s
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1.0 0.5 0 1.0 0.5 0 (a) % Full Bise
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References [1] AL-FARES, M., LOUKIS
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internet applications. ACM SIGCOMM
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when the capacity of basemesh is in
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the topological regularity of Faceb
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Figure 1: High-level system overvie
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metrics can be used to find under-p
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computes a lightweight statistic fo
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0.80 0.60 0.40 0.20 0.05 10 40 60 1
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the majority of traffic is routed a
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8. REFERENCES [1] ab - Apache HTTP
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Syscall latency (nsec) 10 8 10 7 10
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Syscall latency (nsec) 10 7 10 6 10
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Consequently, models can be modifie
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over, the low and bounded latency d
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Latency (¹s) Latency (¹s) 35000 3
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Throughput (10K qps) Latency (ms) 1
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Cumulative Average Error 0.5 0.4 0.
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duced by Clipper’s layered archit
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References [1] M. Abadi, A. Agarwal
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Gaia: Geo-Distributed Machine Learn
Page 643:
Network Bandwidth (Mb/s) 1000 900 8
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ization factorizes X into factor ma
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Normliaed Cost 2.5 2 1.5 1 0.5 0 Ma
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Acknowledgments We thank our shephe
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[55] C. Olston, J. Jiang, and J. Wi
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〈 x˜ t − x ∗ , g˜ t 〉 = 1
Page 658:
inaccurate, values in order to get
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Memcached ETC SYS 100% 95.83 93.87
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Container 1 … Container N Infinis
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Each RDMA dispatch queue has a limi
Page 667 and 668:
We have implemented INFINISWAP as a
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workloads with different rates of S
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Completion time (s) 5000 4000 3000
Page 673 and 674:
[3] Amazon EC2 Pricing. https://aws
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[59] F. Mietke, R. Rex, R. Baumgart
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Send QUERY_MEM messages Receive FRE
Page 680 and 681:
TUX 2 : Distributed Graph Computati
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Model: User 0 rƸ ui = q T i p u Us
Page 684 and 685:
dates. This is because they are sca
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ExchangeStage:: Exchange(v_doc, v_w
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Time per iteration (s) 140 120 100
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Time per iteration (s) 1000 100 10
Page 692 and 693:
[3] Petuum v0.93. https://github.co
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Correct by Construction Networks us
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zone 1 security policy core zone 3
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RouterZoneIn RouterZoneOut zone cor
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possible outputs of ^R on this pack
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vm1 vm2 vm3 vnet 1 vrouter vm4 vm5
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Scale Hosts Switches NetKAT Policy
Page 706 and 707:
References [1] M. Al-Fares, A. Louk
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given in terms of: • a packet p
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Verifying Reachability in Networks
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function with a set of packets to f
Page 714 and 715:
Intrusion Prevention Systems We con
Page 716 and 717:
parallel and origin-agnostic middle
Page 718 and 719:
Time (seconds) 500 450 400 350 300
Page 720 and 721:
1000 1000 Subnet 1 100 100 SB Time
Page 722 and 723:
References [1] AMAZON. Amazon EC2 S
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[56] VELNER, Y., ALPERNAS, K., PAND
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uration parameters that control, e.
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C Formal Definition of Slices Given
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Automated Bug Removal for Software-
Page 732 and 733:
1 FlowTable(@Swi,Hdr,Prt) :- Packet
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h1 Tuple(@C,Tab,Val1,Val2) :- Base(
Page 736 and 737:
function GENERATEREPAIRCANDIDATES(P
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7(v1) FlowTable(@Swi,Hdr,Prt) :- Pa
Page 740 and 741:
Query description Result Q1 H20 is
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Q1 Q2 Q3 Q4 Q5 Trema (Ruby) 7/2 10/
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[33] D. Logothetis, S. De, and K. Y
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To address this problem, this paper
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s 2 s 3 r 1 r 2 r 3 s 1 r 4 s 4 (a)
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in the graph on which link(r) is in
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Algorithm 3 Compute all-pairs reach
Page 755 and 756:
CDF 1 0.8 0.6 0.4 0.2 0 Berkley INE
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48, 26, 35, 17, 33] and online [27,
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[41] NUNES, B. A. A., MENDONCA, M.,
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