Dynamic clamp with StdpC software

Dynamic clamp is a powerful method that allows the introduction of artificial electrical components into target cells to simulate ionic conductances and synaptic inputs. This method is based on a fast cycle of measuring the membrane potential of a cell, calculating the current of a desired simulated component using an appropriate model and injecting this current into the cell. Here we present a dynamic clamp protocol using free, fully integrated, open-source software (StdpC, for spike timing-dependent plasticity clamp). Use of this protocol does not require specialist hardware, costly commercial software, experience in real-time operating systems or a strong programming background. The software enables the configuration and operation of a wide range of complex and fully automated dynamic clamp experiments through an intuitive and powerful interface with a minimal initial lead time of a few hours. After initial configuration, experimental results can be generated within minutes of establishing cell recording

Chance-Constrained ADMM Approach for Decentralized Control of Distributed Energy Resources

Distribution systems are undergoing a dramatic transition from a passive circuit that routinely disseminates electric power among downstream nodes to the system with distributed energy resources. The distributed energy resources come in a variety of technologies and typically include photovoltaic (PV) arrays, thermostatically controlled loads, energy storage units. Often these resources are interfaced with the system via inverters that can adjust active and reactive power injections, thus supporting the operational performance of the system. This paper designs a control policy for such inverters using the local power flow measurements. The control actuates active and reactive power injections of the inverter-based distributed energy resources. This strategy is then incorporated into a chance-constrained, decentralized optimal power flow formulation to maintain voltage levels and power flows within their limits and to mitigate the volatility of (PV) resources

TARANET: Traffic-Analysis Resistant Anonymity at the NETwork layer

Modern low-latency anonymity systems, no matter whether constructed as an overlay or implemented at the network layer, offer limited security guarantees against traffic analysis. On the other hand, high-latency anonymity systems offer strong security guarantees at the cost of computational overhead and long delays, which are excessive for interactive applications. We propose TARANET, an anonymity system that implements protection against traffic analysis at the network layer, and limits the incurred latency and overhead. In TARANET's setup phase, traffic analysis is thwarted by mixing. In the data transmission phase, end hosts and ASes coordinate to shape traffic into constant-rate transmission using packet splitting. Our prototype implementation shows that TARANET can forward anonymous traffic at over 50~Gbps using commodity hardware

Chameleon: A Hybrid Secure Computation Framework for Machine Learning Applications

We present Chameleon, a novel hybrid (mixed-protocol) framework for secure function evaluation (SFE) which enables two parties to jointly compute a function without disclosing their private inputs. Chameleon combines the best aspects of generic SFE protocols with the ones that are based upon additive secret sharing. In particular, the framework performs linear operations in the ring $\mathbb{Z}_{2^l}$ using additively secret shared values and nonlinear operations using Yao's Garbled Circuits or the Goldreich-Micali-Wigderson protocol. Chameleon departs from the common assumption of additive or linear secret sharing models where three or more parties need to communicate in the online phase: the framework allows two parties with private inputs to communicate in the online phase under the assumption of a third node generating correlated randomness in an offline phase. Almost all of the heavy cryptographic operations are precomputed in an offline phase which substantially reduces the communication overhead. Chameleon is both scalable and significantly more efficient than the ABY framework (NDSS'15) it is based on. Our framework supports signed fixed-point numbers. In particular, Chameleon's vector dot product of signed fixed-point numbers improves the efficiency of mining and classification of encrypted data for algorithms based upon heavy matrix multiplications. Our evaluation of Chameleon on a 5 layer convolutional deep neural network shows 133x and 4.2x faster executions than Microsoft CryptoNets (ICML'16) and MiniONN (CCS'17), respectively

Efficient Bit-Decomposition and Modulus-Conversion Protocols with an Honest Majority

We propose secret-sharing-based bit-decomposition and modulus conversion protocols for a prime order ring $\mathbb{Z}_p$ with an honest majority: an adversary can corrupt $k-1$ parties of $n$ parties and $2k-1 \le n$. Our protocols are secure against passive and active adversaries depending on the components of our protocols. We assume a secret is an $\ell$-bit element and $2^{\ell+\lceil \log m \rceil} < p$, where $m= k$ in the passive security and $m= \binom{n}{k-1}$ in the active security. The outputs of our bit-decomposition and modulus-conversion protocols are $\ell$ tuple of shares in $\mathbb{Z}_2$ and a share in $\mathbb{Z}_{p\u27}$, respectively, where $p\u27$ is the modulus to be converted. If $k$ and $n$ are small, the communication complexity of our passively secure bit-decomposition and modulus-conversion protocols are $O(\ell)$ bits and $O(\lceil \log p\u27 \rceil)$ bits, respectively. Our key observation is that a quotient of additive shares can be computed from the \emph{least} significant $\lceil \log m \rceil$ bits. If a secret $a$ is ``shifted\u27\u27 and additively shared by $x_i$ in $\mathbb{Z}_p$ as $2^{\lceil \log m \rceil}a = \sum_{i=0}^{m-1} x_i = 2^{ \lceil \log m \rceil} a + qp$, the least significant $\lceil \log m \rceil$ bits of $\sum_{i=0}^{m-1} x_i$ determines $q$ since $p$ is an odd prime and the least significant $\lceil \log m \rceil$ bits of $2^{\lceil \log m \rceil} a$ are $0$s

StdpC: a modern dynamic clamp

With the advancement of computer technology many novel uses of dynamic clamp have become possible. We have added new features to our dynamic clamp software StdpC (“Spike timing-dependent plasticity Clamp”) allowing such new applications while conserving the ease of use and installation of the popular earlier Dynclamp 2/4 package. Here, we introduce the new features of a waveform generator, freely programmable Hodgkin–Huxley conductances, learning synapses, graphic data displays, and a powerful scripting mechanism and discuss examples of experiments using these features. In the first example we built and ‘voltage clamped’ a conductance based model cell from a passive resistor–capacitor (RC) circuit using the dynamic clamp software to generate the voltage-dependent currents. In the second example we coupled our new spike generator through a burst detection/burst generation mechanism in a phase-dependent way to a neuron in a central pattern generator and dissected the subtle interaction between neurons, which seems to implement an information transfer through intraburst spike patterns. In the third example, making use of the new plasticity mechanism for simulated synapses, we analyzed the effect of spike timing-dependent plasticity (STDP) on synchronization revealing considerable enhancement of the entrainment of a post-synaptic neuron by a periodic spike train. These examples illustrate that with modern dynamic clamp software like StdpC, the dynamic clamp has developed beyond the mere introduction of artificial synapses or ionic conductances into neurons to a universal research tool, which might well become a standard instrument of modern electrophysiology

Practical Two-party Computational Differential Privacy with Active Security.

Distributed models for differential privacy (DP), such as the local and shuffle models, allow for differential privacy without having to trust a single central dataholder. They do however typically require adding more noise than the central model. One commonly iterated remark is that achieving DP with similar accuracy as in the central model is directly achievable by \textit{emulating the trusted party}, using general multiparty computation (MPC), which computes a canonical DP mechanism such as the Laplace or Gaussian mechanism. There have been a few works proposing concrete protocols for doing this but as of yet, all of them either require honest majorities, only allow passive corruptions, only allow computing aggregate functions, lack formal claims of what type of DP is achieved or are not computable in polynomial time by a finite computer. In this work, we propose the first efficiently computable protocol for emulating a dataholder running the geometric mechanism, and which retains its security and DP properties in the presence of dishonest majorities and active corruptions. To this end, we first analyse why current definitions of computational DP are unsuitable for this setting and introduce a new version of computational DP, SIM$^*$-CDP. We then demonstrate the merit of this new definition by proving that our protocol satisfies it. Further, we use the protocol to compute two-party inner products with computational DP and with similar levels of accuracy as in the central model, being the first to do so. Finally, we provide an open-sourced implementation of our protocol and benchmark its practical performance