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<p>With the popularity of mobile health monitoring and genome sequencing techniques, the scale of biomedical and genomic data grow rapidly, with their privacy receiving more and more concerns. Encryption technique plays an important role in many aspects of security guarantee for these data. For mobile devices, encryption is even more important yet more challenging, as these devices are usually used in environments that may not be well protected and have rather limited computing resources. With heterogeneous multi-core processors becoming popular on mobile devices to satisfy different needs of applications, designing heterogeneous algorithms to harness all the available resources are tricky but have the potential to deliver high performance. In this research, we study how to design the heterogeneous version for AES algorithm, a representative encryption algorithm, on such processor to improve throughput and energy efficiency. To alleviate the overhead, we proposed a hybrid strategy to firstly find optimal workload allocations for cores of the processor in the offline stage and then dynamically adjust the balance in the online stage to match the running environment. We do a series of experiments on common genome data, with results showing 25–400% improvements in throughput, and 5.5–2800% improvements in energy efficiency.</p>

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Heterogeneous bioinformatic data encryption on portable devices

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年4月3日

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  • Hao Chen,
  • Xiayun Hong,
  • Yao Cheng,
  • Xiaotong Wang,
  • Liyan Chen,
  • Xuan Cheng,
  • Juncong Lin

摘要

With the popularity of mobile health monitoring and genome sequencing techniques, the scale of biomedical and genomic data grow rapidly, with their privacy receiving more and more concerns. Encryption technique plays an important role in many aspects of security guarantee for these data. For mobile devices, encryption is even more important yet more challenging, as these devices are usually used in environments that may not be well protected and have rather limited computing resources. With heterogeneous multi-core processors becoming popular on mobile devices to satisfy different needs of applications, designing heterogeneous algorithms to harness all the available resources are tricky but have the potential to deliver high performance. In this research, we study how to design the heterogeneous version for AES algorithm, a representative encryption algorithm, on such processor to improve throughput and energy efficiency. To alleviate the overhead, we proposed a hybrid strategy to firstly find optimal workload allocations for cores of the processor in the offline stage and then dynamically adjust the balance in the online stage to match the running environment. We do a series of experiments on common genome data, with results showing 25–400% improvements in throughput, and 5.5–2800% improvements in energy efficiency.