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Helios: Huffman Coding Based Lightweight Encryption Scheme for Data Transmission Mazharul Islam1, Novia Nurain2, Mohammad Kaykobad3, Sriram Chellappan4, A

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Helios: Huffman Coding Based Lightweight Encryption Scheme for Data Transmission Mazharul Islam1, Novia Nurain2, Mohammad Kaykobad3, Sriram Chellappan4, A HEliOS: Huffman Coding Based Lightweight Encryption Scheme for Data Transmission Mazharul Islam1, Novia Nurain2, Mohammad Kaykobad3, Sriram Chellappan4, A. B. M. Alim Al Islam5 Bangladesh University of Engineering and Technology, Dhaka, Bangladesh1, 2, 3, 5 University of South Florida, FL, USA4 {mazharul, novia}@cse.uiu.ac.bd1, 2, {kaykobad, alim_razi}@cse.buet.ac.bd3, 5, [email protected] Abstract 1 Introduction Demand for fast data sharing among smart devices is rapidly in- Recent proliferation in data connectivity along with low pricing of creasing. This trend creates challenges towards ensuring essential hardware devices have enabled smart devices with many applica- security for online shared data while maintaining the resource tions. These applications tend to stay coherently synced to a cloud usage at a reasonable level. Existing research studies attempt to server, and enable sharing a diverse range of multimedia and textual leverage compression based encryption for enabling such secure data covering audio, video, emails, sensor data (from environment and fast data transmission replacing the traditional resource-heavy and infrastructure such as rail lines), health data (such as heart rate, encryption schemes. Current compression-based encryption meth- body movements, sleep-wake time and body temperature) etc. Most ods mainly focus on error insensitive digital data formats and prone digital data today are highly sensitive as well as confidential. These to be vulnerable to different attacks. Therefore, in this paper, we pro- applications demand fast and energy efficient security mechanisms pose and implement a new Huffman compression based Encryption to protect their data. scheme using lightweight dynamic Order Statistic tree (HEliOS) In order to implement security mechanisms for shared data, for digital data transmission. The core idea of HEliOS involves we generally need to apply encryption to the transmitted data. around finding a secure encoding method based on a novel notion However, encryption is often a resource-expensive task, which con- of Huffman coding, which compresses the given digital data using sumes a lot of CPU cycles and valuable resources. In this realm, a small sized “secret" (called as secret_intelligence in our study). existing studies [1, 5, 25] have come up with lightweight crypto- HEliOS does this in such a way that, without the possession of the graphic schemes to ensure secure communication without demand- secret intelligence, an attacker will not be able to decode the en- ing substantial time and resource usage to secure the data before coded compressed data. Hence, by encrypting only the small-sized transmission. However, the level of security gets sacrificed with intelligence, we can secure the whole compressed data. Moreover, these lightweight schemes in most of the cases. Moreover, with the our rigorous real experimental evaluation for downloading and up- growing demand of fast connectivity for these devices, surprisingly loading digital data to and from a personal cloud storage Dropbox manufacturers have taken a retroactive step by trading off security server validates efficacy and lightweight nature of HEliOS. to meet the demands of fast connectivity. Do et al., [11] have re- cently presented a case study on Samsung Gear Live smart-watch CCS Concepts showing that recent smart devices store and transmit a wide range • Security and privacy → Software and application security of unsecured sensitive data, which are vulnerable to attacks and ; Web application security. exfiltration. In this regard, designing compression based encryption schemes Keywords can appear as a promising approach solution [14, 16, 20, 24, 26, compression, encryption, Huffman encoding, order statistic trees. 29, 30, 37, 38]. Here, by combining compression and encryption techniques, we can achieve security, computational and energy ACM Reference Format: savings in two steps as shown on Fig. 1. Firstly, we can apply encod- Mazharul Islam1, Novia Nurain2, Mohammad Kaykobad3, Sriram Chellappan4, ing to compress to the data using a small-sized . A. B. M. Alim Al Islam5. 2019. HEliOS: Huffman Coding Based Lightweight secret_intelligence We introduce the in such a way that it becomes Encryption Scheme for Data Transmission. In Proceedings of 16th EAI In- secret_intelligence impossible (or extremely difficult) for an attacker to decode the ternational Conference on Mobile and Ubiquitous Systems: Computing, Net- working and Services (MobiQuitous). ACM, Houston, TX, USA, 10 pages. compressed encoded data without knowing the secret_intelligence. https://doi.org/10.1145/3360774.3360829 Secondly, we secure the encoded compressed data simply by en- crypting the small-sized secret_intelligence, which is used in the encoding mechanism. From a cryptographer’s point of view, we Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed can relate the secret_intelligence to the notion of key used in tra- for profit or commercial advantage and that copies bear this notice and the full citation ditional encryption standards. Just like without the key we can on the first page. Copyrights for components of this work owned by others than ACM not decrypt the encrypted data in traditional encryption standards, must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a without the secret_intelligence we cannot decompress (nor restore) fee. Request permissions from [email protected]. the compressed data in our case. Therefore, by securing only the MobiQuitous, November 12–14, 2019, Houston, TX, USA secret_intelligence that is required for decompressing the data, we © 2019 Association for Computing Machinery. ACM ISBN 978-1-4503-7283-1/19/11...$15.00 can make the whole compressed data as meaningless as encrypted https://doi.org/10.1145/3360774.3360829 data would appear to attackers. Existing encoding schemes that MobiQuitous, November 12–14, 2019, Houston, TX, USA Islam, et al. can be used to compress the data in this regard are either error 2 insensitive (e.g., Compressive Sensing [4]), vulnerable to attacks (e.g., Multiple Huffman Tree [37] and Swapped Huffman Tree [20]), Plain text 1 or suffer from poor scalability (e.g., Chaotic Huffman Tree [16]). To this extent, in this paper, we propose and implement a new Figure 1: Block diagram of a compression based encryption scheme. Huffman compression based Encryption scheme using lightweight dynamic rder tatistics trees ( ) for data transmission. HE- the Eqn. y = Φx + z which is to multiply x with the projection O S HEliOS m×n liOS builds a dynamic order statistic tree using a secret_intelligence matrix Φ 2 R under the condition that x is m −compressible in and uses this dynamic order statistic tree to encode and compress some basis Ψ. While decompressing, we can get an estimated xˆ from ˆ ˆ plain text data. For secured and fast data transmission, HEliOS en- xˆ = Ψθ where we find the optimized θ subjected to minimizing ˆ crypts only the secret_intelligence using receiver’s public key and jjy − ΦΨθ jj. The reconstruction error jjxˆ − x jj depends on how sends the encoded compressed data along with it. The receiver, large m is in Φ. and which basis Ψ is chosen. These two terms are then, can first decrypt the secret_intelligence with his/her pri- expressed in terms of sensing matrix A = ΨΦ. One key point to vate key to build the same dynamic order statistic tree using the note here is that CS is lossy compression sacrificing a small amount secret_intelligence. Subsequently, the receiver can decode the en- of reconstruction error while decompressing the compressed data coded data using the built dynamic order statistic tree. Form an on the receiver’s side even with the correct sensing matrix A. As attacker’s point of view, without the possession of the receiver’s a result, CS as a means for encryption standard can only work private key, the attacker can not figure out the secret intelligence for data that is not accuracy sensitive, but will fail when smart and build the order statistic tree. Without the order statistic tree, devices need to communicate on accuracy sensitive data (e.g., text, the encoded data remains non-decodable and safe from the attacker. physiological signals etc.) where we cannot accept any error. Hence Based on our study, we make the following key contributions in CS based encryption schemes proposed in [14, 24, 26, 29, 30, 38] this paper: are not suitable for error sensitive data. • We present a novel compression based encryption scheme 2.2 Huffman Based Encryption Schemes HEliOS, which can be used as a new encryption standard for data sharing. HEliOS can meet the demand of fast online file The classical Huffman compression coding [17] assigns prefix bi- sharing in a secured way. nary strings of 0’s and 1’s to represent each symbol in the com- pressed data. The assignment of binary strings is optimal in the • We present theoretical analysis on security and performance of HEliOS through several lemmas and their proofs. sense the symbols with the highest frequencies, are assigned the binary string having lowest lengths and hence the plain text is • We implement HEliOS in two different real experimental scenarios. One of them covers mobile and laptop clients con- compressed. Fig. 2 illustrates one such classical Huffman tree for nected to a personal cloud storage Dropbox server. The other the plain text "aabcacbddbaaa". The symbol ‘a’ which has the fre- one covers sharing files in a wired P2P network compris- quency 6 is assigned the lowest length binary encoding string “0”. ing several machines. Our experimental results demonstrate In the same way, the next higher frequency symbols ‘b’, ‘c’, ‘d’ (with that HEliOS achieves significantly faster transmission and frequencies 3; 2; 2) are assigned encoding strings “10”; “110”; “1111” reception of data in a secured manner for a range of digital respectively.
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