Homomorphic Encryption (HE), first constructed in 2009, is a class of encryption schemes that ena... more Homomorphic Encryption (HE), first constructed in 2009, is a class of encryption schemes that enables computation over encrypted data. Variants of HE in the context of multiple parties have led to the development of two different lines of HE schemes-Multi-Party Homomorphic Encryption (MPHE) and Multi-Key Homomorphic Encryption (MKHE). These primitives cater to different applications and each approach has its own pros and cons. At a high level, MPHE schemes tend to be much more efficient but require the set of computing parties to be fixed throughout the entire operation, frequently a limiting assumption. On the other hand, MKHE schemes tend to have poor scaling (quadratic) with the number of parties but allow us to add new parties to the joint computation anytime since they support computation between ciphertexts under different keys. In this work, we formalize a new variant of HE called Multi-Group Homomorphic Encryption (MGHE). Stated informally, an MGHE scheme provides a seamless integration between MPHE and MKHE, and combines the best of both these primitives. In an MGHE scheme, a group of parties generates a public key jointly which results in the compact ciphertexts and efficient homomorphic operations, similar to MPHE. However, unlike MPHE, it also supports computations on encrypted data under different keys, a property enjoyed by MKHE schemes. We provide a concrete construction of such an MGHE scheme from the BFV scheme. The public key generation procedure of our scheme is fully non-interactive so that the set of computing parties does not have to be determined and no information about other parties is needed in advance of individual key generation. At the heart of our construction is a novel refactoring of the relinearization key to avoid interaction as typically needed. We also implement our scheme and demonstrate that the this generalization does not incur any additional overhead and in fact, can be more performant than existing MPHE and MKHE schemes.
CHEMCAD is a powerful and effective software tool for chemical process simulation. Evaporator con... more CHEMCAD is a powerful and effective software tool for chemical process simulation. Evaporator consists of a heat exchanger for boiling the solution with special provisions for separation of liquid and vapour phases.Calandria Evaporator also called as short-tube vertical evaporators are widely used in industries.In this paper, the vibration analysis for acalandria evaporator is carried out using CHEMCAD. The change in the values of cross flow velocity, critical velocity, vortex shedding frequency, turbulent buffeting frequency and natural frequencythroughout the length of tube is noted and graphs are plotted for every vibration mechanism against the tube length. It is observed that these vibration mechanisms increase from inlet to the center but decrease as they approach to the outlet. This kind of analysis is reported to be one of the basic ways to study the vibration mechanisms incalandria evaporator.
Synthesizing the emerging directions of research at the intersection of differential privacy and ... more Synthesizing the emerging directions of research at the intersection of differential privacy and cryptography.
Applications of machine learning have become increasingly common in recent years. For instance, n... more Applications of machine learning have become increasingly common in recent years. For instance, navigation systems like Google Maps use machine learning to better predict traffic patterns; Facebook, LinkedIn, and other social media platforms use machine learning to customize user's news feeds. Central to all these systems is user data. However, the sensitive nature of the collected data has also led to a number of privacy concerns. Privacy-preserving machine learning enables systems that can
Right to be forgotten, also known as the right to erasure, is the right of individuals to have th... more Right to be forgotten, also known as the right to erasure, is the right of individuals to have their data erased from an entity storing it. The General Data Protection Regulation in the European Union legally solidified the status of this long held notion. As a consequence, there is a growing need for the development of mechanisms whereby users can verify if service providers comply with their deletion requests. In this work, we take the first step in proposing a formal framework to study the design of such verification mechanisms for data deletion requests -- also known as machine unlearning -- in the context of systems that provide machine learning as a service. We propose a backdoor-based verification mechanism and demonstrate its effectiveness in certifying data deletion with high confidence using the above framework. Our mechanism makes a novel use of backdoor attacks in ML as a basis for quantitatively inferring machine unlearning. In our mechanism, each user poisons part of i...
In the present scenario, weight reduction has been the main focus of automobile industries. The m... more In the present scenario, weight reduction has been the main focus of automobile industries. The manufacturing of a mono-leaf spring made up of composite material could improve the high unsprung weight, poor ride quality and passenger comfort. Characteristics of good suspension includes minimum tyre wear, minimum wheel cost, low initial cost, simplicity of construction, low unsprung weight and minimum maintenance etc. The objective is to give the idea about suitable composite materials, useful manufacturing techniques, advantages and limitations of composite leaf spring over the steel leaf spring. The introduction of fiber reinforced plastics (FRP) has made it possible to reduced weight of the machine element without any reduction of the load carrying capacity and high strength-to-weight ratio compared with those of steel, multi-leaf steel springs are being replaced by mono leaf FRP spring.
Modern processors, e.g., Intel SGX, allow applications to isolate secret code and data in encrypt... more Modern processors, e.g., Intel SGX, allow applications to isolate secret code and data in encrypted memory regions called enclaves. While encryption effectively hides the contents of memory, the sequence of address references issued by the secret code leaks information. This is a serious problem because these leaks can easily break the confidentiality guarantees of enclaves. In this paper, we explore Oblivious RAM (ORAM) designs that prevent these information leaks under the constraints of modern SGX processors. Most ORAMs are a poor fit for these processors because they have high constant overhead factors or require large private memories, which are not available in these processors. We address these limitations with a new hierarchical ORAM construction, the Pyramid ORAM, that is optimized towards online bandwidth cost and small blocks. It uses a new hashing scheme that circumvents the complexity of previous hierarchical schemes. We present an efficient x64-optimized implementation...
Secure comparison has been a fundamental challenge in privacypreserving computation, since its in... more Secure comparison has been a fundamental challenge in privacypreserving computation, since its inception as Yao's millionaires' problem (FOCS 1982). In this work, we present a novel construction for general nparty private comparison, secure against an active adversary, in the dishonest majority setting. For the case of comparisons over fields, our protocol is more efficient than the best prior work (edaBits: Crypto 2020), with "1.5ˆbetter throughput in most adversarial settings, over 2.3ˆbetter throughput in particular in the passive, honest majority setting, and lower communication. Our comparisons crucially eliminate the need for bounded inputs as well as the need for statistical security that prior works require. An important consequence of removing this "slack" (a gap between the bit-length of the input and the MPC representation) is that multi-party computation (MPC) protocols can be run in a field of smaller size, reducing the overhead incurred by privacy-preserving computations. We achieve this novel construction using the commutative nature of addition over rings and fields. This makes the protocol both simple to implement and highly efficient and we provide an implementation in MP-SPDZ (CCS 2020).
Computing on data in a manner that preserve the privacy is of growing importance. Multi-Party Com... more Computing on data in a manner that preserve the privacy is of growing importance. Multi-Party Computation (MPC) and Homomorphic Encryption (HE) are two cryptographic techniques for privacypreserving computations. In this work, we have developed efficient UCsecure multiparty protocols for matrix multiplications and two-dimensional convolutions. We built upon the SPDZ framework and integrated the state-of-the-art HE algorithms for matrix multiplication. Our protocol achieved communication cost linear only in the input and output dimensions and not on the number of multiplication operations. We eliminate the "triple sacrifice" step of SPDZ to improve efficiency and simplify the zero-knowledge proofs. We implemented our protocols and benchmarked them against the SPDZ LowGear variant (Keller et al. Eurocrypt'18). For multiplying two square matrices of size 128, we reduced the communication cost from 1.54 GB to 12.46 MB, an improvement of over two orders of magnitude that only improves with larger matrix sizes. For evaluating all convolution layers of the ResNet-50 neural network, the communication reduces cost from 5 TB to 41 GB.
Water generated from the fish processing industry is contaminated with organic matter. This organ... more Water generated from the fish processing industry is contaminated with organic matter. This organic matter present in wastewater increases the biochemical oxygen demand (BOD) and chemical oxygen demand (COD). A new technology, hydrodynamic cavitation (HC) is used to deal with this wastewater produced in fish processing plants. The orifice plate is used in the HC reactor to generate a cavitation effect. The intensification of this technique was carried out with the help of hydrogen peroxide (H2O2) and TiO2. The treatment of this wastewater is reported in terms of percent degradation in BOD and COD and in biodegradability index (BI). Operating parameters like inlet pressure, pH, operating temperature and H2O2 doses were used to find the optimum condition. 15 g/L of H2O2 gave 69.5% reduction of COD in the 120 min of treatment that also increases BI value to 0.93 at inlet pressure 8 bar, Plate-5, temperature (30 °C), and pH 4. In the ultrasonic cavitation (UC) reactor, COD reduction is ...
2017 IEEE International Symposium on High Performance Computer Architecture (HPCA), 2017
Information leaks based on timing side channels in computing devices have serious consequences fo... more Information leaks based on timing side channels in computing devices have serious consequences for user security and privacy. In particular, malicious applications in multiuser systems such as data centers and cloud-computing environments can exploit memory timing as a side channel to infer a victim's program access patterns/phases. Memory timing channels can also be exploited for covert communications by an adversary. We propose Camouflage, a hardware solution to mitigate timing channel attacks not only in the memory system, but also along the path to and from the memory system (e.g. NoC, memory scheduler queues). Camouflage introduces the novel idea of shaping memory requests' and responses' interarrival time into a predetermined distribution for security purposes, even creating additional fake traffic if needed. This limits untrusted parties (either cloud providers or coscheduled clients) from inferring information from another security domain by probing the bus to and from memory, or analyzing memory response rate. We design three different memory traffic shaping mechanisms for different security scenarios by having Camouflage work on requests, responses, and bi-directional (both) traffic. Camouflage is complementary to ORAMs and can be optionally used in conjunction with ORAMs to protect information leaks via both memory access timing and memory access patterns. Camouflage offers a tunable trade-off between system security and system performance. We evaluate Camouflage's security and performance both theoretically and via simulations, and find that Camouflage outperforms state-of-the-art solutions in performance by up to 50%.
Proceedings on Privacy Enhancing Technologies, 2021
We propose Falcon, an end-to-end 3-party protocol for efficient private training and inference of... more We propose Falcon, an end-to-end 3-party protocol for efficient private training and inference of large machine learning models. Falcon presents four main advantages – (i) It is highly expressive with support for high capacity networks such as VGG16 (ii) it supports batch normalization which is important for training complex networks such as AlexNet (iii) Falcon guarantees security with abort against malicious adversaries, assuming an honest majority (iv) Lastly, Falcon presents new theoretical insights for protocol design that make it highly efficient and allow it to outperform existing secure deep learning solutions. Compared to prior art for private inference, we are about 8× faster than SecureNN (PETS’19) on average and comparable to ABY3 (CCS’18). We are about 16 − 200× more communication efficient than either of these. For private training, we are about 6× faster than SecureNN, 4.4× faster than ABY3 and about 2−60× more communication efficient. Our experiments in the WAN setti...
Proceedings on Privacy Enhancing Technologies, 2018
In this work, we investigate if statistical privacy can enhance the performance of ORAM mechanism... more In this work, we investigate if statistical privacy can enhance the performance of ORAM mechanisms while providing rigorous privacy guarantees. We propose a formal and rigorous framework for developing ORAM protocols with statistical security viz., a differentially private ORAM (DP-ORAM). We present Root ORAM, a family of DP-ORAMs that provide a tunable, multi-dimensional trade-off between the desired bandwidth overhead, local storage and system security. We theoretically analyze Root ORAM to quantify both its security and performance. We experimentally demonstrate the benefits of Root ORAM and find that (1) Root ORAM can reduce local storage overhead by about 2× for a reasonable values of privacy budget, significantly enhancing performance in memory limited platforms such as trusted execution environments, and (2) Root ORAM allows tunable trade-offs between bandwidth, storage, and privacy, reducing bandwidth overheads by up to 2×-10× (at the cost of increased storage/statistical pr...
In this work degradation of Acid Red-18 (AR-18) was examined in the hydrodynamic cavitation react... more In this work degradation of Acid Red-18 (AR-18) was examined in the hydrodynamic cavitation reactor. Orifice plates with different holes geometry are used to determine the optimum plate to carry out the degradation based on cavitation number. The obtained optimum orifice plate is used as a cavitating device on varying parameters like initial AR-18 concentrations, pH, temperatures, and operating pressures of the reactor. A photocatalyst (TiO 2) was prepared by the sol-gel method and used in combination with H 2 O 2 to intensify the degradation of AR-18. The obtained optimum condition of hydrodynamic cavitation was again used in the ultrasonic cavitation reactor for the comparison. Hydrodynamic cavitation (orifice) given the highest degradation as compared to Hydrodynamic cavitation (Venturi) and Ultrasonic Cavitation with and without the use of TiO 2. At TiO 2 (300 mg/L) dose, 88.1%, 70.4% and 64.8% degradation is obtained in HC-O, HC-V and UC reactor at initial AR-18 concentration (15 ppm), pH (3), Operating temperature (35 C), and H 2 O 2 (300 mg/L). Hence the use of an advanced oxidation process can be successfully used with hydrodynamic cavitation to intensify the degradation of Acid Red-18 under the controlled operating parameters.
Proceedings on Privacy Enhancing Technologies, 2019
The popularity of Tor has made it an attractive target for a variety of deanonymization and finge... more The popularity of Tor has made it an attractive target for a variety of deanonymization and fingerprinting attacks. Location-based path selection algorithms have been proposed as a countermeasure to defend against such attacks. However, adversaries can exploit the location-awareness of these algorithms by strategically placing relays in locations that increase their chances of being selected as a client’s guard. Being chosen as a guard facilitates website fingerprinting and traffic correlation attacks over extended time periods. In this work, we rigorously define and analyze the guard placement attack. We present novel guard placement attacks and show that three state-of-the-art path selection algorithms—Counter-RAPTOR, DeNASA, and LASTor—are vulnerable to these attacks, overcoming defenses considered by all three systems. For instance, in one attack, we show that an adversary contributing only 0.216% of Tor’s total bandwidth can attain an average selection probability of 18.22%, 84...
Proceedings on Privacy Enhancing Technologies, 2019
Neural Networks (NN) provide a powerful method for machine learning training and inference. To ef... more Neural Networks (NN) provide a powerful method for machine learning training and inference. To effectively train, it is desirable for multiple parties to combine their data – however, doing so conflicts with data privacy. In this work, we provide novel three-party secure computation protocols for various NN building blocks such as matrix multiplication, convolutions, Rectified Linear Units, Maxpool, normalization and so on. This enables us to construct three-party secure protocols for training and inference of several NN architectures such that no single party learns any information about the data. Experimentally, we implement our system over Amazon EC2 servers in different settings. Our work advances the state-of-the-art of secure computation for neural networks in three ways: 1. Scalability: We are the first work to provide neural network training on Convolutional Neural Networks (CNNs) that have an accuracy of > 99% on the MNIST dataset; 2. Performance: For secure inference, o...
Homomorphic Encryption (HE), first constructed in 2009, is a class of encryption schemes that ena... more Homomorphic Encryption (HE), first constructed in 2009, is a class of encryption schemes that enables computation over encrypted data. Variants of HE in the context of multiple parties have led to the development of two different lines of HE schemes-Multi-Party Homomorphic Encryption (MPHE) and Multi-Key Homomorphic Encryption (MKHE). These primitives cater to different applications and each approach has its own pros and cons. At a high level, MPHE schemes tend to be much more efficient but require the set of computing parties to be fixed throughout the entire operation, frequently a limiting assumption. On the other hand, MKHE schemes tend to have poor scaling (quadratic) with the number of parties but allow us to add new parties to the joint computation anytime since they support computation between ciphertexts under different keys. In this work, we formalize a new variant of HE called Multi-Group Homomorphic Encryption (MGHE). Stated informally, an MGHE scheme provides a seamless integration between MPHE and MKHE, and combines the best of both these primitives. In an MGHE scheme, a group of parties generates a public key jointly which results in the compact ciphertexts and efficient homomorphic operations, similar to MPHE. However, unlike MPHE, it also supports computations on encrypted data under different keys, a property enjoyed by MKHE schemes. We provide a concrete construction of such an MGHE scheme from the BFV scheme. The public key generation procedure of our scheme is fully non-interactive so that the set of computing parties does not have to be determined and no information about other parties is needed in advance of individual key generation. At the heart of our construction is a novel refactoring of the relinearization key to avoid interaction as typically needed. We also implement our scheme and demonstrate that the this generalization does not incur any additional overhead and in fact, can be more performant than existing MPHE and MKHE schemes.
CHEMCAD is a powerful and effective software tool for chemical process simulation. Evaporator con... more CHEMCAD is a powerful and effective software tool for chemical process simulation. Evaporator consists of a heat exchanger for boiling the solution with special provisions for separation of liquid and vapour phases.Calandria Evaporator also called as short-tube vertical evaporators are widely used in industries.In this paper, the vibration analysis for acalandria evaporator is carried out using CHEMCAD. The change in the values of cross flow velocity, critical velocity, vortex shedding frequency, turbulent buffeting frequency and natural frequencythroughout the length of tube is noted and graphs are plotted for every vibration mechanism against the tube length. It is observed that these vibration mechanisms increase from inlet to the center but decrease as they approach to the outlet. This kind of analysis is reported to be one of the basic ways to study the vibration mechanisms incalandria evaporator.
Synthesizing the emerging directions of research at the intersection of differential privacy and ... more Synthesizing the emerging directions of research at the intersection of differential privacy and cryptography.
Applications of machine learning have become increasingly common in recent years. For instance, n... more Applications of machine learning have become increasingly common in recent years. For instance, navigation systems like Google Maps use machine learning to better predict traffic patterns; Facebook, LinkedIn, and other social media platforms use machine learning to customize user's news feeds. Central to all these systems is user data. However, the sensitive nature of the collected data has also led to a number of privacy concerns. Privacy-preserving machine learning enables systems that can
Right to be forgotten, also known as the right to erasure, is the right of individuals to have th... more Right to be forgotten, also known as the right to erasure, is the right of individuals to have their data erased from an entity storing it. The General Data Protection Regulation in the European Union legally solidified the status of this long held notion. As a consequence, there is a growing need for the development of mechanisms whereby users can verify if service providers comply with their deletion requests. In this work, we take the first step in proposing a formal framework to study the design of such verification mechanisms for data deletion requests -- also known as machine unlearning -- in the context of systems that provide machine learning as a service. We propose a backdoor-based verification mechanism and demonstrate its effectiveness in certifying data deletion with high confidence using the above framework. Our mechanism makes a novel use of backdoor attacks in ML as a basis for quantitatively inferring machine unlearning. In our mechanism, each user poisons part of i...
In the present scenario, weight reduction has been the main focus of automobile industries. The m... more In the present scenario, weight reduction has been the main focus of automobile industries. The manufacturing of a mono-leaf spring made up of composite material could improve the high unsprung weight, poor ride quality and passenger comfort. Characteristics of good suspension includes minimum tyre wear, minimum wheel cost, low initial cost, simplicity of construction, low unsprung weight and minimum maintenance etc. The objective is to give the idea about suitable composite materials, useful manufacturing techniques, advantages and limitations of composite leaf spring over the steel leaf spring. The introduction of fiber reinforced plastics (FRP) has made it possible to reduced weight of the machine element without any reduction of the load carrying capacity and high strength-to-weight ratio compared with those of steel, multi-leaf steel springs are being replaced by mono leaf FRP spring.
Modern processors, e.g., Intel SGX, allow applications to isolate secret code and data in encrypt... more Modern processors, e.g., Intel SGX, allow applications to isolate secret code and data in encrypted memory regions called enclaves. While encryption effectively hides the contents of memory, the sequence of address references issued by the secret code leaks information. This is a serious problem because these leaks can easily break the confidentiality guarantees of enclaves. In this paper, we explore Oblivious RAM (ORAM) designs that prevent these information leaks under the constraints of modern SGX processors. Most ORAMs are a poor fit for these processors because they have high constant overhead factors or require large private memories, which are not available in these processors. We address these limitations with a new hierarchical ORAM construction, the Pyramid ORAM, that is optimized towards online bandwidth cost and small blocks. It uses a new hashing scheme that circumvents the complexity of previous hierarchical schemes. We present an efficient x64-optimized implementation...
Secure comparison has been a fundamental challenge in privacypreserving computation, since its in... more Secure comparison has been a fundamental challenge in privacypreserving computation, since its inception as Yao's millionaires' problem (FOCS 1982). In this work, we present a novel construction for general nparty private comparison, secure against an active adversary, in the dishonest majority setting. For the case of comparisons over fields, our protocol is more efficient than the best prior work (edaBits: Crypto 2020), with "1.5ˆbetter throughput in most adversarial settings, over 2.3ˆbetter throughput in particular in the passive, honest majority setting, and lower communication. Our comparisons crucially eliminate the need for bounded inputs as well as the need for statistical security that prior works require. An important consequence of removing this "slack" (a gap between the bit-length of the input and the MPC representation) is that multi-party computation (MPC) protocols can be run in a field of smaller size, reducing the overhead incurred by privacy-preserving computations. We achieve this novel construction using the commutative nature of addition over rings and fields. This makes the protocol both simple to implement and highly efficient and we provide an implementation in MP-SPDZ (CCS 2020).
Computing on data in a manner that preserve the privacy is of growing importance. Multi-Party Com... more Computing on data in a manner that preserve the privacy is of growing importance. Multi-Party Computation (MPC) and Homomorphic Encryption (HE) are two cryptographic techniques for privacypreserving computations. In this work, we have developed efficient UCsecure multiparty protocols for matrix multiplications and two-dimensional convolutions. We built upon the SPDZ framework and integrated the state-of-the-art HE algorithms for matrix multiplication. Our protocol achieved communication cost linear only in the input and output dimensions and not on the number of multiplication operations. We eliminate the "triple sacrifice" step of SPDZ to improve efficiency and simplify the zero-knowledge proofs. We implemented our protocols and benchmarked them against the SPDZ LowGear variant (Keller et al. Eurocrypt'18). For multiplying two square matrices of size 128, we reduced the communication cost from 1.54 GB to 12.46 MB, an improvement of over two orders of magnitude that only improves with larger matrix sizes. For evaluating all convolution layers of the ResNet-50 neural network, the communication reduces cost from 5 TB to 41 GB.
Water generated from the fish processing industry is contaminated with organic matter. This organ... more Water generated from the fish processing industry is contaminated with organic matter. This organic matter present in wastewater increases the biochemical oxygen demand (BOD) and chemical oxygen demand (COD). A new technology, hydrodynamic cavitation (HC) is used to deal with this wastewater produced in fish processing plants. The orifice plate is used in the HC reactor to generate a cavitation effect. The intensification of this technique was carried out with the help of hydrogen peroxide (H2O2) and TiO2. The treatment of this wastewater is reported in terms of percent degradation in BOD and COD and in biodegradability index (BI). Operating parameters like inlet pressure, pH, operating temperature and H2O2 doses were used to find the optimum condition. 15 g/L of H2O2 gave 69.5% reduction of COD in the 120 min of treatment that also increases BI value to 0.93 at inlet pressure 8 bar, Plate-5, temperature (30 °C), and pH 4. In the ultrasonic cavitation (UC) reactor, COD reduction is ...
2017 IEEE International Symposium on High Performance Computer Architecture (HPCA), 2017
Information leaks based on timing side channels in computing devices have serious consequences fo... more Information leaks based on timing side channels in computing devices have serious consequences for user security and privacy. In particular, malicious applications in multiuser systems such as data centers and cloud-computing environments can exploit memory timing as a side channel to infer a victim's program access patterns/phases. Memory timing channels can also be exploited for covert communications by an adversary. We propose Camouflage, a hardware solution to mitigate timing channel attacks not only in the memory system, but also along the path to and from the memory system (e.g. NoC, memory scheduler queues). Camouflage introduces the novel idea of shaping memory requests' and responses' interarrival time into a predetermined distribution for security purposes, even creating additional fake traffic if needed. This limits untrusted parties (either cloud providers or coscheduled clients) from inferring information from another security domain by probing the bus to and from memory, or analyzing memory response rate. We design three different memory traffic shaping mechanisms for different security scenarios by having Camouflage work on requests, responses, and bi-directional (both) traffic. Camouflage is complementary to ORAMs and can be optionally used in conjunction with ORAMs to protect information leaks via both memory access timing and memory access patterns. Camouflage offers a tunable trade-off between system security and system performance. We evaluate Camouflage's security and performance both theoretically and via simulations, and find that Camouflage outperforms state-of-the-art solutions in performance by up to 50%.
Proceedings on Privacy Enhancing Technologies, 2021
We propose Falcon, an end-to-end 3-party protocol for efficient private training and inference of... more We propose Falcon, an end-to-end 3-party protocol for efficient private training and inference of large machine learning models. Falcon presents four main advantages – (i) It is highly expressive with support for high capacity networks such as VGG16 (ii) it supports batch normalization which is important for training complex networks such as AlexNet (iii) Falcon guarantees security with abort against malicious adversaries, assuming an honest majority (iv) Lastly, Falcon presents new theoretical insights for protocol design that make it highly efficient and allow it to outperform existing secure deep learning solutions. Compared to prior art for private inference, we are about 8× faster than SecureNN (PETS’19) on average and comparable to ABY3 (CCS’18). We are about 16 − 200× more communication efficient than either of these. For private training, we are about 6× faster than SecureNN, 4.4× faster than ABY3 and about 2−60× more communication efficient. Our experiments in the WAN setti...
Proceedings on Privacy Enhancing Technologies, 2018
In this work, we investigate if statistical privacy can enhance the performance of ORAM mechanism... more In this work, we investigate if statistical privacy can enhance the performance of ORAM mechanisms while providing rigorous privacy guarantees. We propose a formal and rigorous framework for developing ORAM protocols with statistical security viz., a differentially private ORAM (DP-ORAM). We present Root ORAM, a family of DP-ORAMs that provide a tunable, multi-dimensional trade-off between the desired bandwidth overhead, local storage and system security. We theoretically analyze Root ORAM to quantify both its security and performance. We experimentally demonstrate the benefits of Root ORAM and find that (1) Root ORAM can reduce local storage overhead by about 2× for a reasonable values of privacy budget, significantly enhancing performance in memory limited platforms such as trusted execution environments, and (2) Root ORAM allows tunable trade-offs between bandwidth, storage, and privacy, reducing bandwidth overheads by up to 2×-10× (at the cost of increased storage/statistical pr...
In this work degradation of Acid Red-18 (AR-18) was examined in the hydrodynamic cavitation react... more In this work degradation of Acid Red-18 (AR-18) was examined in the hydrodynamic cavitation reactor. Orifice plates with different holes geometry are used to determine the optimum plate to carry out the degradation based on cavitation number. The obtained optimum orifice plate is used as a cavitating device on varying parameters like initial AR-18 concentrations, pH, temperatures, and operating pressures of the reactor. A photocatalyst (TiO 2) was prepared by the sol-gel method and used in combination with H 2 O 2 to intensify the degradation of AR-18. The obtained optimum condition of hydrodynamic cavitation was again used in the ultrasonic cavitation reactor for the comparison. Hydrodynamic cavitation (orifice) given the highest degradation as compared to Hydrodynamic cavitation (Venturi) and Ultrasonic Cavitation with and without the use of TiO 2. At TiO 2 (300 mg/L) dose, 88.1%, 70.4% and 64.8% degradation is obtained in HC-O, HC-V and UC reactor at initial AR-18 concentration (15 ppm), pH (3), Operating temperature (35 C), and H 2 O 2 (300 mg/L). Hence the use of an advanced oxidation process can be successfully used with hydrodynamic cavitation to intensify the degradation of Acid Red-18 under the controlled operating parameters.
Proceedings on Privacy Enhancing Technologies, 2019
The popularity of Tor has made it an attractive target for a variety of deanonymization and finge... more The popularity of Tor has made it an attractive target for a variety of deanonymization and fingerprinting attacks. Location-based path selection algorithms have been proposed as a countermeasure to defend against such attacks. However, adversaries can exploit the location-awareness of these algorithms by strategically placing relays in locations that increase their chances of being selected as a client’s guard. Being chosen as a guard facilitates website fingerprinting and traffic correlation attacks over extended time periods. In this work, we rigorously define and analyze the guard placement attack. We present novel guard placement attacks and show that three state-of-the-art path selection algorithms—Counter-RAPTOR, DeNASA, and LASTor—are vulnerable to these attacks, overcoming defenses considered by all three systems. For instance, in one attack, we show that an adversary contributing only 0.216% of Tor’s total bandwidth can attain an average selection probability of 18.22%, 84...
Proceedings on Privacy Enhancing Technologies, 2019
Neural Networks (NN) provide a powerful method for machine learning training and inference. To ef... more Neural Networks (NN) provide a powerful method for machine learning training and inference. To effectively train, it is desirable for multiple parties to combine their data – however, doing so conflicts with data privacy. In this work, we provide novel three-party secure computation protocols for various NN building blocks such as matrix multiplication, convolutions, Rectified Linear Units, Maxpool, normalization and so on. This enables us to construct three-party secure protocols for training and inference of several NN architectures such that no single party learns any information about the data. Experimentally, we implement our system over Amazon EC2 servers in different settings. Our work advances the state-of-the-art of secure computation for neural networks in three ways: 1. Scalability: We are the first work to provide neural network training on Convolutional Neural Networks (CNNs) that have an accuracy of > 99% on the MNIST dataset; 2. Performance: For secure inference, o...
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Papers by sameer wagh