90 research outputs found
Efficient Deep Reinforcement Learning via Adaptive Policy Transfer
Transfer Learning (TL) has shown great potential to accelerate Reinforcement
Learning (RL) by leveraging prior knowledge from past learned policies of
relevant tasks. Existing transfer approaches either explicitly computes the
similarity between tasks or select appropriate source policies to provide
guided explorations for the target task. However, how to directly optimize the
target policy by alternatively utilizing knowledge from appropriate source
policies without explicitly measuring the similarity is currently missing. In
this paper, we propose a novel Policy Transfer Framework (PTF) to accelerate RL
by taking advantage of this idea. Our framework learns when and which source
policy is the best to reuse for the target policy and when to terminate it by
modeling multi-policy transfer as the option learning problem. PTF can be
easily combined with existing deep RL approaches. Experimental results show it
significantly accelerates the learning process and surpasses state-of-the-art
policy transfer methods in terms of learning efficiency and final performance
in both discrete and continuous action spaces.Comment: Accepted by IJCAI'202
Aegis: A Lightning Fast Privacy-preserving Machine Learning Platform against Malicious Adversaries
Privacy-preserving machine learning (PPML) techniques have gained significant popularity in the past years. Those protocols have been widely adopted in many real-world security-sensitive machine learning scenarios, e.g., medical care and finance. In this work, we introduce ~-- a high-performance PPML platform built on top of a maliciously secure 3-PC framework over ring . In particular, we propose a novel 2-round secure comparison (a.k.a., sign bit extraction) protocol in the preprocessing model. The communication of its semi-honest version is only 25% of the state-of-the-art (SOTA) constant-round semi-honest comparison protocol by Zhou et al.(S&P 2023); both communication and round complexity of its malicious version are approximately 50% of the SOTA (BLAZE) by Patra and Suresh (NDSS 2020), for .
Moreover, the communication of our maliciously secure inner product protocol is merely bits, reducing 50% from the SOTA (Swift) by Koti et al. (USENIX 2021).
Finally, the resulting ReLU and MaxPool PPML protocols outperform the SOTA by in the semi-honest setting and in the malicious setting, respectively
Multi-party Private Function Evaluation for RAM
Private function evaluation (PFE) is a special type of MPC protocols that, in addition to the input privacy, can preserve the function privacy. In this work, we propose a PFE scheme for RAM. In particular, we first design an efficient 4-server distributed ORAM scheme with amortized communication per access (both reading and writing). We then simulate a RISC RAM machine over the MPC platform, hiding (i) the memory access pattern, (ii) the machine state (including registers, program counter, condition flag, etc.), and (iii) the executed instructions. Our scheme can naturally support a simplified TinyRAM instruction set; if a public RAM program with given inputs needs to execute instruction cycles, our PFE scheme is able to securely evaluate on private and within online rounds. We prototype and benchmark our system for set intersection, binary search, quicksort, and heapsort algorithms. For instance, to obliviously perform the binary search algorithm on a array takes with function privacy
UC Secure Private Branching Program and Decision Tree Evaluation
Branching program (BP) is a DAG-based non-uniform computational model for L/poly class. It has been widely used in formal verification, logic synthesis, and data analysis. As a special BP, a decision tree is a popular machine learning classifier for its effectiveness and simplicity. In this work, we propose a UC-secure efficient 3-party computation platform for outsourced branching program and/or decision tree evaluation. We construct a constant-round protocol and a linear-round protocol. In particular, the overall (online + offline) communication cost of our linear-round protocol is and its round complexity is , where is the DAG size, is the number of features, is the feature length, and is the longest path length. To enable efficient oblivious hopping among the DAG nodes, we propose a lightweight -out-of- shared OT protocol with logarithmic communication in both online and offline phase. This partial result may be of independent interest to some other cryptographic protocols. Our benchmark shows, compared with the state-of-the-arts, the proposed constant-round protocol is up to 10X faster in the WAN setting, while the proposed linear-round protocol is up to 15X faster in the LAN setting
Simultaneous shield and buffer insertion for crosstalk noise reduction in global routing
Abstract We present a method for incorporating crosstalk reduction criteri
Buffering global interconnects in structured ASIC design
Structured ASICs present an attractive alternative to reducing design costs and turnaround times in nanometer designs. As with conventional ASICs, such designs require global wires to be buffered. However, via-programmable designs must prefabricate and preplace buffers in the layout. This paper proposes a novel and accurate statistical estimation technique for distributing prefabricated buffers through a layout. It employs Rent’s rule to estimate the buffer distribution required for the layout, so that an appropriate structured ASIC may be selected for the design. Experimental results show that the buffer distribution estimation is accurate and economic, and that a uniform buffer distribution can maintain a high degree of regularity in design and shows a good timing performance, comparable with nonuniform buffer distribution. Key words: Structured ASIC, Rent’s rule, buffer insertion, interconnect, physical design
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