3 Search Results for "Yu, Haifeng"

Document

DOI: 10.4230/LIPIcs.ECRTS.2024.13

DeepTrust^RT: Confidential Deep Neural Inference Meets Real-Time!

Authors: Mohammad Fakhruddin Babar and Monowar Hasan

Published in: LIPIcs, Volume 298, 36th Euromicro Conference on Real-Time Systems (ECRTS 2024)

Abstract

Deep Neural Networks (DNNs) are becoming common in "learning-enabled" time-critical applications such as autonomous driving and robotics. One approach to protect DNN inference from adversarial actions and preserve model privacy/confidentiality is to execute them within trusted enclaves available in modern processors. However, running DNN inference inside limited-capacity enclaves while ensuring timing guarantees is challenging due to (a) large size of DNN workloads and (b) extra switching between "normal" and "trusted" execution modes. This paper introduces new time-aware scheduling schemes - DeepTrust^RT - to securely execute deep neural inferences for learning-enabled real-time systems. We first propose a variant of EDF (called DeepTrust^RT-LW) that slices each DNN layer and runs them sequentially in the enclave. However, due to extra context switch overheads of individual layer slices, we further introduce a novel layer fusion technique (named DeepTrust^RT-FUSION). Our proposed scheme provides hard real-time guarantees by fusing multiple layers of DNN workload from multiple tasks; thus allowing them to fit and run concurrently within the enclaves while maintaining real-time guarantees. We implemented and tested DeepTrust^RT ideas on the Raspberry Pi platform running OP-TEE+DarkNet-TZ DNN APIs and three DNN workloads (AlexNet-squeezed, Tiny Darknet, YOLOv3-tiny). Compared to the layer-wise partitioning approach (DeepTrust^RT-LW), DeepTrust^RT-FUSION can schedule up to 3x more tasksets and reduce context switches by up to 11.12x. We further demonstrate the efficacy of DeepTrust^RT using a flight controller (ArduPilot) case study and find that DeepTrust^RT-FUSION retains real-time guarantees where DeepTrust^RT-LW becomes unschedulable.

Cite as

Mohammad Fakhruddin Babar and Monowar Hasan. DeepTrust^RT: Confidential Deep Neural Inference Meets Real-Time!. In 36th Euromicro Conference on Real-Time Systems (ECRTS 2024). Leibniz International Proceedings in Informatics (LIPIcs), Volume 298, pp. 13:1-13:24, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)

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@InProceedings{babar_et_al:LIPIcs.ECRTS.2024.13,
  author =	{Babar, Mohammad Fakhruddin and Hasan, Monowar},
  title =	{{DeepTrust^RT: Confidential Deep Neural Inference Meets Real-Time!}},
  booktitle =	{36th Euromicro Conference on Real-Time Systems (ECRTS 2024)},
  pages =	{13:1--13:24},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-324-9},
  ISSN =	{1868-8969},
  year =	{2024},
  volume =	{298},
  editor =	{Pellizzoni, Rodolfo},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ECRTS.2024.13},
  URN =		{urn:nbn:de:0030-drops-203161},
  doi =		{10.4230/LIPIcs.ECRTS.2024.13},
  annote =	{Keywords: DNN, TrustZone, Real-Time Systems}
}

Document

Track A: Algorithms, Complexity and Games

DOI: 10.4230/LIPIcs.ICALP.2024.44

Bayesian Calibrated Click-Through Auctions

Authors: Junjie Chen, Minming Li, Haifeng Xu, and Song Zuo

Published in: LIPIcs, Volume 297, 51st International Colloquium on Automata, Languages, and Programming (ICALP 2024)

Abstract

We study information design in click-through auctions, in which the bidders/advertisers bid for winning an opportunity to show their ads but only pay for realized clicks. The payment may or may not happen, and its probability is called the click-through rate (CTR). This auction format is widely used in the industry of online advertising. Bidders have private values, whereas the seller has private information about each bidder’s CTRs. We are interested in the seller’s problem of partially revealing CTR information to maximize revenue. Information design in click-through auctions turns out to be intriguingly different from almost all previous studies in this space since any revealed information about CTRs will never affect bidders' bidding behaviors - they will always bid their true value per click - but only affect the auction’s allocation and payment rule. In some sense, this makes information design effectively a constrained mechanism design problem. Our first result is an FPTAS to compute an approximately optimal mechanism under a constant number of bidders. The design of this algorithm leverages Bayesian bidder values which help to "smooth" the seller’s revenue function and lead to better tractability. The design of this FPTAS is complex and primarily algorithmic. Our second main result pursues the design of "simple" mechanisms that are approximately optimal yet more practical. We primarily focus on the two-bidder situation, which is already notoriously challenging as demonstrated in recent works. When bidders' CTR distribution is symmetric, we develop a simple prior-free signaling scheme, whose construction relies on a parameter termed optimal signal ratio. The constructed scheme provably obtains a good approximation as long as the maximum and minimum of bidders' value density functions do not differ much.

Cite as

Junjie Chen, Minming Li, Haifeng Xu, and Song Zuo. Bayesian Calibrated Click-Through Auctions. In 51st International Colloquium on Automata, Languages, and Programming (ICALP 2024). Leibniz International Proceedings in Informatics (LIPIcs), Volume 297, pp. 44:1-44:18, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)

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@InProceedings{chen_et_al:LIPIcs.ICALP.2024.44,
  author =	{Chen, Junjie and Li, Minming and Xu, Haifeng and Zuo, Song},
  title =	{{Bayesian Calibrated Click-Through Auctions}},
  booktitle =	{51st International Colloquium on Automata, Languages, and Programming (ICALP 2024)},
  pages =	{44:1--44:18},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-322-5},
  ISSN =	{1868-8969},
  year =	{2024},
  volume =	{297},
  editor =	{Bringmann, Karl and Grohe, Martin and Puppis, Gabriele and Svensson, Ola},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ICALP.2024.44},
  URN =		{urn:nbn:de:0030-drops-201878},
  doi =		{10.4230/LIPIcs.ICALP.2024.44},
  annote =	{Keywords: information design, ad auctions, online advertising, mechanism design}
}

Document

DOI: 10.4230/LIPIcs.DISC.2017.29

Some Lower Bounds in Dynamic Networks with Oblivious Adversaries

Authors: Irvan Jahja, Haifeng Yu, and Yuda Zhao

Published in: LIPIcs, Volume 91, 31st International Symposium on Distributed Computing (DISC 2017)

Abstract

This paper considers several closely-related problems in synchronous dynamic networks with oblivious adversaries, and proves novel Omega(d + poly(m)) lower bounds on their time complexity (in rounds). Here d is the dynamic diameter of the dynamic network and m is the total number of nodes. Before this work, the only known lower bounds on these problems under oblivious adversaries were the trivial Omega(d) lower bounds. Our novel lower bounds are hence the first non-trivial lower bounds and also the first lower bounds with a poly(m) term. Our proof relies on a novel reduction from a certain two-party communication complexity problem. Our central proof technique is unique in the sense that we consider the communication complexity with a special leaker. The leaker helps Alice and Bob in the two-party problem, by disclosing to Alice and Bob certain "non-critical" information about the problem instance that they are solving.

Cite as

Irvan Jahja, Haifeng Yu, and Yuda Zhao. Some Lower Bounds in Dynamic Networks with Oblivious Adversaries. In 31st International Symposium on Distributed Computing (DISC 2017). Leibniz International Proceedings in Informatics (LIPIcs), Volume 91, pp. 29:1-29:16, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2017)

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@InProceedings{jahja_et_al:LIPIcs.DISC.2017.29,
  author =	{Jahja, Irvan and Yu, Haifeng and Zhao, Yuda},
  title =	{{Some Lower Bounds in Dynamic Networks with Oblivious Adversaries}},
  booktitle =	{31st International Symposium on Distributed Computing (DISC 2017)},
  pages =	{29:1--29:16},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-053-8},
  ISSN =	{1868-8969},
  year =	{2017},
  volume =	{91},
  editor =	{Richa, Andr\'{e}a},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.DISC.2017.29},
  URN =		{urn:nbn:de:0030-drops-79690},
  doi =		{10.4230/LIPIcs.DISC.2017.29},
  annote =	{Keywords: dynamic networks, oblivious adversary, adaptive adversary, lower bounds, communication complexity}
}

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1 Babar, Mohammad Fakhruddin
1 Chen, Junjie
1 Hasan, Monowar
1 Jahja, Irvan
1 Li, Minming
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1 Computer systems organization → Real-time systems
1 Security and privacy → Systems security
1 Theory of computation → Algorithmic game theory and mechanism design

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1 DNN
1 Real-Time Systems
1 TrustZone
1 ad auctions
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